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Cyberbullying detection systems rely increasingly on machine learning techniques. However, class imbalance in cyberbullying datasets, where the percentage of normal labeled classes is higher than that of abnormal labeled ones, presents a significant challenge for classification algorithms. This issue is particularly problematic in two-class datasets, where conventional machine learning methods tend to perform poorly on minority class samples due to the influence of the majority class. To address this problem, researchers have proposed various oversampling and undersampling techniques. In this paper, we investigate the effectiveness of such techniques in addressing class imbalance in cyberbullying datasets. We conduct an experimental study that involves a preprocessing step to enhance machine learning algorithm performance. We then examine the impact of imbalanced data on classification performance for four cyberbullying datasets. To study the classification performance on balanced cyberbullying datasets, we employ four resampling techniques, namely random undersampling, random oversampling, SMOTE, and SMOTE + TOMEK. We evaluate the impact of each rebalancing technique on classification performance using eight well-known classification algorithms. Our findings demonstrate that the performance of resampling techniques depends on the dataset size, imbalance ratio, and classifier used. The conducted experiments proved that there are no techniques that will always perform better the others. [ABSTRACT FROM AUTHOR]

With the increased use of social network sites, such as Twitter, attackers exploit these platforms to spread counterfeit content. Such content can be fake advertisements or illegal content. Classifying such content is a challenging task, especially in Arabic. The Arabic language has a complex structure and makes classification tasks more difficult. This paper presents an approach to classifying Arabic tweets using classical machine learning (non-deep machine learning) and deep learning techniques. Tweets corpus were collected through Twitter API and labelled manually to get a reliable dataset. For an efficient classifier, feature extraction is applied to the corpus dataset. Then, two learning techniques are used for each feature extraction technique on the created dataset using N-gram models (uni-gram, bi-gram, and char-gram). The applied classical machine learning algorithms are support vector machines, neural networks, logistics regression, and naïve Bayes. Global vector (GloVe) and fastText learning models are utilised for the deep learning approaches. The Precision, Recall, and F1-score are the suggested performance measures calculated in this paper. Afterwards, the dataset is increased using the synthetic minority oversampling technique class to create a balanced dataset. After applying the classical machine learning models, the experimental results show that the neural network algorithm outperforms the other algorithms. Moreover, the GloVe outperforms the fastText model for the deep learning approach. [ABSTRACT FROM AUTHOR]

Twitter produces a massive amount of data due to its popularity that is one of the reasons underlying big data problems. One of those problems is the classification of tweets due to use of sophisticated and complex language, which makes the current tools insufficient. We present our framework HTwitt, built on top of the Hadoop ecosystem, which consists of a MapReduce algorithm and a set of machine learning techniques embedded within a big data analytics platform to efficiently address the following problems: (1) traditional data processing techniques are inadequate to handle big data; (2) data preprocessing needs substantial manual effort; (3) domain knowledge is required before the classification; (4) semantic explanation is ignored. In this work, these challenges are overcome by using different algorithms combined with a Naïve Bayes classifier to ensure reliability and highly precise recommendations in virtualization and cloud environments. These features make HTwitt different from others in terms of having an effective and practical design for text classification in big data analytics. The main contribution of the paper is to propose a framework for building landslide early warning systems by pinpointing useful tweets and visualizing them along with the processed information. We demonstrate the results of the experiments which quantify the levels of overfitting in the training stage of the model using different sizes of real-world datasets in machine learning phases. Our results demonstrate that the proposed system provides high-quality results with a score of nearly 95% and meets the requirement of a Hadoop-based classification system. [ABSTRACT FROM AUTHOR]

Skin cancer, primarily resulting from the abnormal growth of skin cells, is among the most common cancer types. In recent decades, the incidence of skin cancer cases worldwide has risen significantly (one in every three newly diagnosed cancer cases is a skin cancer). Such an increase can be attributed to changes in our social and lifestyle habits coupled with devastating man-made alterations to the global ecosystem. Despite such a notable increase, diagnosis of skin cancer is still challenging, which becomes critical as its early detection is crucial for increasing the overall survival rate. This calls for advancements of innovative computer-aided systems to assist medical experts with their decision making. In this context, there has been a recent surge of interest in machine learning (ML), in particular, deep neural networks (DNNs), to provide complementary assistance to expert physicians. While DNNs have a high processing capacity far beyond that of human experts, their outputs are deterministic, i.e., providing estimates without prediction confidence. Therefore, it is of paramount importance to develop DNNs with uncertainty-awareness to provide confidence in their predictions. Monte Carlo dropout (MCD) is vastly used for uncertainty quantification; however, MCD suffers from overconfidence and being miss calibrated. In this paper, we use MCD algorithm to develop an uncertainty-aware DNN that assigns high predictive entropy to erroneous predictions and enable the model to optimize the hyper-parameters during training, which leads to more accurate uncertainty quantification. We use two synthetic (two moons and blobs) and a real dataset (skin cancer) to validate our algorithm. Our experiments on these datasets prove effectiveness of our approach in quantifying reliable uncertainty. Our method achieved 85.65 ± 0.18 prediction accuracy, 83.03 ± 0.25 uncertainty accuracy, and 1.93 ± 0.3 expected calibration error outperforming vanilla MCD and MCD with loss enhanced based on predicted entropy. [ABSTRACT FROM AUTHOR]

One of the most challenging issues in pattern recognition is the data attribution selection process. Feature selection plays a key role in solving problems with high-dimensional data and is a fundamental step in pre-processing many classifications and machine learning problems. The feature selection method reduces the amount of data and increases the category precision. Unrelated data, which can lead to inappropriate classification, is thus removed to obtain fewer features. In this paper, the Binary Gray Wolf Optimization algorithm uses the Wrapper method for feature selection. The transfer function is an essential part of BGWO to map a continuous value to a binary value. In this study, eight transfer functions are divided into two families: S-shaped and V-shaped. Previous research has used only one transfer function for the whole algorithm, and all wolves in the whole algorithm deal with this transfer function. In this paper, each wolf has its own transfer function. Because algorithms are evolutionary meta-innovations and can optimize themselves, each wolf can play a role in the whole algorithm at any stage while optimizing itself and adapting to its community, and not just depend on one transfer function. In the proposed method, eight transfer functions are divided into two families, S-shaped and V-shaped. This article proposes two approaches for learning the transfer function, by selecting the transfer function and the slope of these functions. In the first approach, we add three or two binary bits to the initial population. If two bits are added, four modes of the transfer function are available, and if three bits are added, eight transfer functions are achievable. These bits are used as a criterion for selecting a predefined transfer function for each wolf. So, in the proposed method, each wolf has its transfer function. During the implementation of the algorithm, the wolves update their position according to the evaluation function and learning. In the second approach, ten or twenty-one binary bits are added to the initial population. If ten binary bits are used, we will have a transfer function, and 2 10 coefficient modes are available for the slope of the transfer function. If twenty-one binary bits are used, we have two transfer functions available. So, there are 2 10 modes for the gradient of the transfer function. These bits are used as a criterion for selecting the transfer function and the coefficient affecting the slope of these functions. In both ideas, after each iteration of the algorithm, the position of the wolves is updated and based on the evaluation function, the alpha wolf is identified and the transfer function is selected. With subsequent iterations, the algorithm learns and optimizes the transfer function to achieve the best feature selection with the smallest error. Experimental results on ten UCI datasets show that selecting the obtained feature subset with high classification accuracy is efficient. [ABSTRACT FROM AUTHOR]

To reliably and accurately classify complicated "big" datasets, machine learning models must be continually improved. This research proposes straightforward yet competitive neural networks for text classification, even though graph neural networks (GNN) have reignited interest in graph-based text classification models. Convolutional neural networks (CNN), artificial neural networks (ANN), and their refined "fine-tuned" models (denoted as FT-CNN and FT-ANN) are the names given to our proposed models. The models presented in this paper demonstrate that our simple models like (CNN, ANN, FT-CNN, and FT-ANN) can perform better than more complex GNN ones such as (SGC, SSGC, and TextGCN) and are comparable to others (i.e., HyperGAT and Bert). The process of fine-tuning is also highly recommended because it improves the performance and reliability of models. The performance of our suggested models on five benchmark datasets (namely, Reuters (R8), R52, 20NewsGroup, Ohsumed, and Mr) is vividly illustrated. According to the experimental findings, on the majority of the target datasets, these models—especially those that have been fine-tuned—perform surprisingly better than SOTA approaches, including GNN-based models. [ABSTRACT FROM AUTHOR]

Big data is a large set of data that is analyzed with the calculation to manifest myriad sources. Big data is capable of handling various challenges to processing huge amounts of data. To handle issues based on large-scale databases, a MapReduce framework is employed which provides robust and simple infrastructure for huge datasets. This paper proposes a novel Elastic collision seeker optimization based Faster R-CNN (ECSO-FRCNN) classifier for efficient big data classification. The proposed ECSO-FRCNN classifier is capable of handling missing attributes, and incremental learning and improves training performance effectively. As the proposed technique deals with large data samples, it necessitates the inclusion of the MapReduce framework. The adaption of MapReduce design in big data classification prevents the classification results from uncertainties such as data redundancy, misclassification, and storage issues. The proposed method is examined with three standard datasets, namely the skin segmentation dataset, mushroom dataset, and localization dataset, collected from the University of California, UCI machine learning repository. Finally, extensive experimental analysis is carried out for various parameters to depict the efficiency of the system. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients' lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. [ABSTRACT FROM AUTHOR]

Uniform Resource Locator (URL) is a unique identifier composed of protocol and domain name used to locate and retrieve a resource on the Internet. Like any Internet service, URLs (also called websites) are vulnerable to compromise by attackers to develop Malicious URLs that can exploit/devastate the user's information and resources. Malicious URLs are usually designed with the intention of promoting cyber-attacks such as spam, phishing, malware, and defacement. These websites usually require action on the user's side and can reach users across emails, text messages, pop-ups, or devious advertisements. They have a potential impact that can reach, in some cases, to compromise the machine or network of the user, especially those arriving by email. Therefore, developing systems to detect malicious URLs is of great interest nowadays. This paper proposes a high-performance machine learning-based detection system to identify Malicious URLs. The proposed system provides two layers of detection. Firstly, we identify the URLs as either benign or malware using a binary classifier. Secondly, we classify the URL classes based on their feature into five classes: benign, spam, phishing, malware, and defacement. Specifically, we report on four ensemble learning approaches, viz. the ensemble of bagging trees (En_Bag) approach, the ensemble of k-nearest neighbor (En_kNN) approach, and the ensemble of boosted decision trees (En_Bos) approach, and the ensemble of subspace discriminator (En_Dsc) approach. The developed approaches have been evaluated on an inclusive and contemporary dataset for uniform resource locators (ISCX-URL2016). ISCX-URL2016 provides a lightweight dataset for detecting and categorizing malicious URLs according to their attack type and lexical analysis. Conventional machine learning evaluation measurements are used to evaluate the detection accuracy, precision, recall, F Score, and detection time. Our experiential assessment indicates that the ensemble of bagging trees (En_Bag) approach provides better performance rates than other ensemble methods. Alternatively, the ensemble of the k-nearest neighbor (En_kNN) approach provides the highest inference speed. We also contrast our En_Bag model with state-of-the-art solutions and show its superiority in binary classification and multi-classification with accuracy rates of 99.3% and 97.92%, respectively. [ABSTRACT FROM AUTHOR]

Data imbalance is one of the most difficult problems in machine learning. The improved ensemble learning model is a promising solution to mitigate this challenge. In this paper, an improved multi-class imbalanced data classification framework is proposed by combining the Focal Loss with Boosting model (FL-Boosting). By addressing the confusion of the second-order derivation of Focal Loss in the traditional ensemble learning model, the proposed model achieves a more efficient and accurate classification of the imbalanced data. More specifically, a Highly Adaptive Focal Loss (HAFL) is proposed to ensure that the model maintains lasting attention to the minority samples, which could be combined with boosting model to build HAFL-Boosting to achieve better performance. The framework has the scalability to adapt to different situations according to typical ensemble learning algorithms such as LightGBM, XGBoost and CatBoost. In addition, to implement the application of the proposed framework on deep models, a two-stage classification method combining ConvNeXt with the improved boosting model is proposed, which could improve the recognition ability to high-dimensional imbalanced data. We evaluate the HAFL-Boosting and the two-stage class imbalance classification method by ablation experiments and benchmark experiments, which demonstrated that the proposed methods obviously improved the scores on several evaluation indexes. The comparative experiments with the latest classification models show that the proposed methods could achieve leading performance from multiple perspectives. [ABSTRACT FROM AUTHOR]

When it comes to offering song suggestions to a user, the music genre is one of the most used tags considered by music streaming services. Motivated by the growing number of songs available, automatic music genre classification systems have become a valuable tool for the creation of user-personalised playlists. Considering that feature engineering represents a major task to be addressed when one develops such systems, this work discusses the generation of new handcrafted features over songs, originally exploring high-order features' moments combined with their derivatives. Additionally, this paper proposes a new wrapper-based selection procedure rigorously based on statistical tests to identify a subset of features that maximise the performance of these systems, irrespective of the classification approach adopted, named Robust Selector of Basis Feature Sets. Based on a synergistic combination of both strategies, a compact subset with 81 features is derived over the GTZAN Dataset. When compared with alternative solutions, this feature set boosted the classification accuracy in datasets containing a wide range of music genres, such as ISMIR2004, BALLROOM, HOMBURG, and FMA Datasets. [ABSTRACT FROM AUTHOR]

In order to improve the diagnosis and classification effect of pituitary tumors, this paper combines the current common machine learning methods and classification prediction methods to improve the traditional machine learning algorithms. Moreover, this paper analyzes the feature algorithm based on the feature extraction requirements of pituitary tumor pictures and compares a variety of commonly used algorithms to select a classification algorithm suitable for the model of this paper through comparison methods. In addition, this paper carries out the calculation of the prediction algorithm and verifies the algorithm according to the actual situation. Finally, based on the neural network algorithm, this paper designs and constructs the algorithm function module and combines the actual needs of pituitary tumors to build the model and verify the performance of the model. The research results show that the model system constructed in this paper meets the expected demand. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is one of the most common inflammatory neurological diseases in young adults. There are three types of MS: (1) In relapsing remitting MS (RRMS), people have temporarily periods of relapses (attacks) for days or weeks, and then symptoms seem to disappear (remitting stage). (2) In secondary progressive MS (SPMS), symptoms worsen more steadily over time. Attacks (relapses) may occur time to time but the disease can progress in non-attack periods. It is estimated that half of the RRMS patients progress to SPMS in 10 years. (3) Primary progressive MS (PPMS) is characterized by slowly worsening symptoms from the beginning, with no relapses or remissions. For PPMS patients, disability progresses slowly. Researchers have found out that in the first year, MS causes more damage than following 5–10 years. Therefore, early diagnosis is vital. In this context, deep learning models started to be popular for assisting identification/diagnosis/classification of MS patients using magnetic resonance imaging (MRI). This paper provides an in-depth review of deep learning approaches for identification and classification of MS using brain MRI images. We discuss recent trends of deep learning methods for MS identification under three categories: CNN models, hybrid models (CNN with a classifier) and deep transfer learning models. Existing deep learning algorithms are analyzed and compared according to their architecture, image modality, pre-processing, feature extraction, classifier, dataset, categories and accuracy. This survey paper would provide a valuable source for researchers who are interested in state-of-the-art deep learning methods for MS identification using MRI images. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) are mimicking computationally powerful biologically inspired models in which neurons communicate through sequences of spikes, regarded here as sparse binary sequences of zeros and ones. In neuroscience it is conjectured that time encoding, where the information is carried by the temporal position of spikes, is playing a crucial role at least in some parts of the brain where estimation of the spiking rate with a large latency cannot take place. Motivated by the efficiency of temporal coding, compared with the widely used rate coding, the goal of this paper is to develop and train an energy-efficient time-coded deep spiking neural network system. To ensure that the similarity among input stimuli is translated into a correlation of the spike sequences, we introduce correlative temporal encoding and extended correlative temporal encoding techniques to map analog input information into input spike patterns. Importantly, we propose an implementation where all multiplications in the system are replaced with at most a few additions. As a more efficient alternative to both rate-coded SNNs and artificial neural networks, such system represents a preferable solution for the implementation of neuromorphic hardware. We consider data classification tasks where input spike patterns are presented to a feed-forward architecture with leaky-integrate-and-fire neurons. The SNN is trained by backpropagation through time with the objective to match sequences of output spikes with those of specifically designed target spike patterns, each corresponding to exactly one class. During inference the target spike pattern with the smallest van Rossum distance from the output spike pattern determines the class. Extensive simulations indicate that the proposed system achieves a classification accuracy at par with that of state-of-the-art machine learning models. [ABSTRACT FROM AUTHOR]

The amount of digital data in the universe is growing at an exponential rate with the rapid development of digital information, and this reveals new machine learning methods. Learning algorithms using hyperboxes are a subsection of machine learning methods. Fuzzy min–max neural network (FMNN) are one of the most common and advanced methods using hyperboxes. FMNN is a special type of NeuroFuzzy system that combines the artificial neural network and fuzzy set into a common framework. This paper conducts an extensive bibliometric and network analysis of FMNN literature. Two hundred and sixty-two publications are analysed from the period of 1992–2022. Several analyses are realized in order to identify trends, challenges and key points in a more scientific and objective way that affect the development of knowledge in the FMNN domain. It can be seen from bibliometric analysis that there is rapid development in the last 10 years. Social network analysis results show that Chee Peng Lim is the most active author in the network. Besides, the modifications of FMNN are generally developed for classification. However, there are still potential future research opportunities for clustering. [ABSTRACT FROM AUTHOR]

The popularity of digital cameras and social networks has greatly enriched people's spiritual life, and we can easily obtain massive amounts of digital photos. However, due to the lack of professional guidance and differences in aesthetic appreciation, the photos taken many photographers lack aesthetics. This article is dedicated to the research of image aesthetics, using computers to simulate human perception, and realize the evaluation or beautification of images in line with human aesthetics. In terms of image classification, this article examines the unique perception of human vision on images and proposes new aesthetic features. Combining visual features and semantic features, the SVM algorithm is utilized to build an aesthetic classifier. In the aspect of image optimization, this paper uses the detection of the main image area and the division line of the area and adjusts the main body size and position of the image according to common aesthetic rules, so as to realize the optimization adjustment of the composition of the social image. The experimental results show that the accuracy of social image classification is 97.7%, and the optimized and adjusted images are more aesthetic. [ABSTRACT FROM AUTHOR]

Multiclass classification is a fundamental and challenging task in machine learning. The existing techniques of multiclass classification can be categorized as (1) decomposition into binary (2) extension from binary and (3) hierarchical classification. Decomposing multiclass classification into a set of binary classifications that can be efficiently solved by using binary classifiers, called class binarization, which is a popular technique for multiclass classification. Neuroevolution, a general and powerful technique for evolving the structure and weights of neural networks, has been successfully applied to binary classification. In this paper, we apply class binarization techniques to a neuroevolution algorithm, NeuroEvolution of Augmenting Topologies (NEAT), that are used to generate neural networks for multiclass classification. We propose a new method that applies Error-Correcting Output Codes (ECOC) to design the class binarization strategies on the neuroevolution for multiclass classification. The ECOC strategies are compared with the class binarization strategies of One-vs-One and One-vs-All on three well-known datasets of Digit, Satellite, and Ecoli. We analyse their performance from four aspects of multiclass classification degradation, accuracy, evolutionary efficiency, and robustness. The results show that the NEAT with ECOC performs high accuracy with low variance. Specifically, it shows significant benefits in a flexible number of binary classifiers and strong robustness. [ABSTRACT FROM AUTHOR]

The monitoring of physical activities and recognition of motion disorders belong to important diagnostical tools in neurology and rehabilitation. The goal of the present paper is in the contribution to this topic by (1) analysis of accelerometric signals recorded by wearable sensors located at specific body positions and by (2) implementation of deep learning methods to classify signal features. This paper uses the general methodology to analysis of accelerometric signals acquired during cycling at different routes followed by the global positioning system. The experimental dataset includes 850 observations that were recorded by a mobile device in the spine area (L3 verterbra) for cycling routes with the different slope. The proposed methodology includes the use of deep learning convolutional neural networks with five layers applied to signal values transformed into the frequency domain without specification of any signal features. The accuracy of discrimination between different motion patterns for the uphill and downhill cycling and recognition of 4 classes associated with different route slopes was 96.6% with the loss criterion of 0.275 for sigmoidal activation functions. These results were compared with those evaluated for selected sets of features estimated for each observation and classified by the support vector machine, Bayesian methods, and the two-layer neural network. The best cross-validation error of 0.361 was achieved for the two-layer neural network model with the sigmoidal and softmax transfer functions. Our methodology suggests that deep learning neural networks are efficient in the assessment of motion activities for automated data processing and have a wide range of applications, including rehabilitation, early diagnosis of neurological problems, and possible use in engineering as well. [ABSTRACT FROM AUTHOR]

Convex optimization techniques are extensively applied to various models, algorithms, and applications of machine learning and data mining. For optimization-based classification methods, the sparsity principle can greatly help to select simple classifier models, while the single- and multi-kernel methods can effectively address nonlinearly separable problems. However, the limited sparsity and kernel methods hinder the improvement of the predictive accuracy, efficiency, iterative update, and interpretable classification model. In this paper, we propose a new Explainable Multi-sparsity Multi-kernel Nonconvex Optimization Least-squares Classifier (EM2NOLC) model, which is an optimization problem with a least-squares objective function and multi-sparsity multi-kernel nonconvex constraints, aiming to address the aforementioned issues. Based on reconstructed multiple kernel learning (MKL), the proposed model can extract important instances and features by finding the sparse coefficient and kernel weight vectors, which are used to compute importance or contribution to classification and obtain the explainable prediction. The corresponding EM2NOLC algorithm is implemented with the Alternating Direction Method of Multipliers (ADMM) method. On the real classification datasets, compared with the three ADMM classifiers of Linear Support Vector Machine Classifier, SVMC, Least Absolute Shrinkage and Selection Operator Classifier, the two MKL classifiers of SimpleMKL and EasyMKL, and the gradient descent classifier of Feature Selection for SVMC, our proposed EM2NOLC generally obtains the best predictive performance and explainable results with the least number of important instances and features having different contribution percentages. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to help bank management in scoring credit card clients using machine learning by modelling and predicting the consumer behaviour concerning three aspects: the probability of single and consecutive missed payments for credit card customers, the purchasing behaviour of customers, and grouping customers based on a mathematical expectation of loss. Two models are developed: the first provides the probability of a missed payment during the next month for each customer, which is described as Missed payment prediction Long Short Term Memory model (MP-LSTM), whilst the second estimates the total monthly amount of purchases, which is defined as Purchase Estimation Prediction Long Short Term Memory model (PE-LSTM). Based on both models, a customer behavioural grouping is provided, which can be helpful for the bank's decision-making. Both models are trained on real credit card transactional datasets. Customer behavioural scores are analysed using classical performance evaluation measures. Calibration analysis of MP-LSTM scores showed that they could be considered as probabilities of missed payments. Obtained purchase estimations were analysed using mean square error and absolute error. The MP-LSTM model was compared to four traditional well-known machine learning algorithms. Experimental results show that, compared with conventional methods based on feature extraction, the consumer credit scoring method based on the MP-LSTM neural network has significantly improved consumer credit scoring. [ABSTRACT FROM AUTHOR]

The extreme learning machine (ELM) is a method to train single-layer feed-forward neural networks that became popular because it uses a fast closed-form expression for training that minimizes the training error with good generalization ability to new data. The ELM requires the tuning of the hidden layer size and the calculation of the pseudo-inverse of the hidden layer activation matrix for the whole training set. With large-scale classification problems, the computational overload caused by tuning becomes not affordable, and the activation matrix is extremely large, so the pseudo-inversion is very slow and eventually the matrix will not fit in memory. The quick extreme learning machine (QELM), proposed in the current paper, is able to manage large classification datasets because it: (1) avoids the tuning by using a bounded estimation of the hidden layer size from the data population; and (2) replaces the training patterns in the activation matrix by a reduced set of prototypes in order to avoid the storage and pseudo-inversion of large matrices. While ELM or even the linear SVM cannot be applied to large datasets, QELM can be executed on datasets up to 31 million data, 30,000 inputs and 131 classes, spending reasonable times (less than 1 h) in general purpose computers without special software nor hardware requirements and achieving performances similar to ELM. [ABSTRACT FROM AUTHOR]

Active Learning Method (ALM) is one of the powerful tools in soft computing and it is inspired by the human brain capabilities in approaching complicated problems. ALM, which is in essence an adaptive fuzzy learning algorithm, tries to model a Multi-Input Single-Output system with several single-input single-output subsystems. Each of these subsystems is then modeled by an ink drop spread (IDS) plane. IDS operator, which is the main processing engine of ALM, extracts two kinds of informative features, Narrow Path and Spread, from each IDS plane without complicated computations. These features from all IDS planes are then aggregated in the inference engine. Despite the great performance of ALM in different applications, an efficient hardware implementation has remained a challenge, which is mainly due to considerably high memory requirement of IDS operation. In this paper, in a novel approach to IDS operation, we propose an abstract representation of the IDS planes which minimizes the memory requirement and the computational cost, and consequently, benefits the hardware implementation in terms of area and speed. The proposed approach is fully compatible with memristor-crossbar implementation with an adaptive learning capability. Simpler learning algorithm and higher speed make our proposed algorithm suitable for applications where real-time process, low-cost and small implementation are of high priority. Applications in the classification of real-world datasets and function approximation are provided to confirm the effectiveness of the algorithm. Eventually, the paper concludes that the proposed computing structure provides a synergy between artificial neural networks and fuzzy domains. [ABSTRACT FROM AUTHOR]

Computational complexity and sample selection are two main factors that limited the performance of online sequential extreme learning machines (OS-ELMs). This paper proposes a new model that introduces the concept of hybrid kernel and sample selection method based on an online learning model using a membership function. In other words, an online sequential extreme learning machine based on a hybrid kernel function (HKOS-ELM) is presented. The algorithm only calculates the kernel function to determine the final output function, mostly solving the computational complexity of the algorithm. The hybrid kernel function proposed in this paper has the advantages of strong learning ability and good generalization performance of single kernel function. Based on the classification essence of the OS-ELM classification, the membership function is introduced into the sample selection to remove the noise point and the outlier point. The experimental results showed that the HKOS-ELM algorithm adding the membership degree with mixed kernel functions preserves the advantages of kernel functions and online learning and improves the classification performance of the system. [ABSTRACT FROM AUTHOR]

The oddity in large information is rising step by step so that the current programming instruments faces trouble in supervision of huge information. Moreover, the pace of the irregularity information in the immense datasets is a key imperative to the exploration business. Along these lines, this paper proposes a novel method for taking care of the large information utilizing Spark structure. The proposed method experiences two stages for arranging the enormous information, which includes highlight choice and arrangement, which is acted in the underlying hubs of Spark engineering. The proposed improvement calculation is named Rider Chaotic Biography streamlining (RCBO) calculation, which is the incorporation of the Rider Optimization Algorithm (ROA) and the standard confused biogeography-based-advancement (CBBO). The proposed RCBO-profound stacked auto-encoder utilizing Spark structure successfully handles the large information for achieving powerful huge information arrangement. Here, the proposed RCBO is utilized for choosing reasonable highlights from the monstrous dataset. Besides, the profound stacked auto-encoder utilizes RCBO for preparing so as to characterize colossal datasets. In this research we focused on problem of supervision related to big information of The Cover type Data in UCI machine learning repository. The dataset describes the forest cover set data to predict the forest cover type from cartographic variables. The dataset is multivariate in nature with number of web hits 263,361. The number of instances is 581012 with 54 numbers of attributes and the task associated for the dataset is classification. The examination of the proposed RCBO-profound stacked auto-encoder-based Spark structure utilizing the UCI AI datasets uncovered that the proposed technique beat different strategies, by procuring maximal exactness of 86.71%, dice coefficient of 92.7%, affectability of 75.2% and explicitness of 95.4% separately. [ABSTRACT FROM AUTHOR]

One-class classification (OCC) needs samples from only a single class to train the classifier. Recently, an auto-associative kernel extreme learning machine was developed for the OCC task. This paper introduces a novel extension of this classifier by embedding minimum variance information within its architecture and is referred to as VAAKELM. The minimum variance embedding forces the network output weights to focus in regions of low variance and reduces the intra-class variance. This leads to a better separation of target samples and outliers, resulting in an improvement in the generalization performance of the classifier. The proposed classifier follows a reconstruction-based approach to OCC and minimizes the reconstruction error by using the kernel extreme learning machine as the base classifier. It uses the deviation in reconstruction error to identify the outliers. We perform experiments on 15 small-size and 10 medium-size one-class benchmark datasets to demonstrate the efficiency of the proposed classifier. We compare the results with 13 existing one-class classifiers by considering the mean F 1 score as the comparison metric. The experimental results show that VAAKELM consistently performs better than the existing classifiers, making it a viable alternative for the OCC task. The source code is available on the GitHub homepage: https://github.com/PratikMishra/VAAKELM. [ABSTRACT FROM AUTHOR]

In recent times, convolutional neural networks became an irreplaceable tool in many different machine learning applications, especially in image classification. On the other hand, new research about robustness and susceptibility of these models to different adversarial attacks has emerged. With the rise in usage and widespread adoption of these models, it is very important to make them suitable for critical applications. In our previous work, we experimented with a new type of learning applicable to all convolutional neural networks: classification based on missing (low-impact) features. In the case of partial inputs/image occlusion, we have shown that our new method creates models that are more robust and perform better when compared to traditional models of the same architecture. In this paper, we explore an interesting characteristic of our newly developed models in that while we see a general increase in validation accuracy, we also lose some important knowledge. We propose one solution to overcome this problem and validate our assumptions against CIFAR-10 image classification dataset. [ABSTRACT FROM AUTHOR]

Misclassification is a critical problem in many machine learning applications. Since even the classifier models with high accuracy (e.g., > 95%) still introduce some misclassification error, it may not be possible to rely on the output of a classifier. In this paper, we introduce trustable learning, which prompts the learning model to yield only the true output, thus avoiding misclassifications. Whenever the model cannot decide the output accurately, the learning model should indicate that there could be a misclassification error if it is forced to classify, and hence, it should reject to make a decision or defer it to a human expert. Therefore, we develop a methodology for trustable learning and apply it to artificial neural networks and show that it is possible to develop a classifier with 0% misclassification error. We propose a novel neural network architecture named WisdomNet that could provide zero prediction error by introducing an additional neuron named as conjugate neuron that would indicate whether the network is able to classify the data correctly or not. The WisdomNet architecture can be applied to any previously built model, and we have evaluated WisdomNet with several network architectures such as multilayer perceptron, convolutional neural network, and deep network on different data sets. The results show that the WisdomNet is able to reduce the classification error rate to 0%, while labeling the data is difficult to classify as 'reject' at a low percentage of within around 10%. [ABSTRACT FROM AUTHOR]

Class-imbalanced datasets are common across several domains such as health, banking, security, and others. The dominance of majority class instances (negative class) often results in biased learning models, and therefore, classifying such datasets requires employing some methods to compact the problem. In this paper, we propose a new hybrid approach aiming at reducing the dominance of the majority class instances using class decomposition and increasing the minority class instances using an oversampling method. Unlike other undersampling methods, which suffer data loss, our method preserves the majority class instances, yet significantly reduces its dominance, resulting in a more balanced dataset and hence improving the results. A large-scale experiment using 60 public datasets was carried out to validate the proposed methods. The results across three standard evaluation metrics show the comparable and superior results with other common and state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

In recent years, classifier ensemble techniques have drawn the attention of many researchers in the machine learning research community. The ultimate goal of these researches is to improve the accuracy of the ensemble compared to the individual classifiers. In this paper, a novel algorithm for building ensembles called dynamic programming-based ensemble design algorithm (DPED) is introduced and studied in detail. The underlying theory behind DPED is based on cooperative game theory in the first phase and applying a dynamic programming approach in the second phase. The main objective of DPED is to reduce the size of the ensemble while encouraging extra diversity in order to improve the accuracy. The performance of the DPED algorithm is compared empirically with the classical ensemble model and with a well-known algorithm called "the most diverse." The experiments were carried out with 13 datasets from UCI and three ensemble models. Each ensemble model is constructed from 15 different base classifiers. The experimental results demonstrate that DPED outperforms the classical ensembles on all datasets in terms of both accuracy and size of the ensemble. Regarding the comparison with the most diverse algorithm, the number of selected classifiers by DPED across all datasets and all domains is less than or equal to the number selected by the most diverse algorithm. Experiment on blog spam dataset, for instance, shows that DPED provides an accuracy of 96.47 compared to 93.87 obtained by the most diverse using 40% training size. Finally, the experimental results verify the reliability, stability, and effectiveness of the proposed DPED algorithm. [ABSTRACT FROM AUTHOR]

Text classification is one of the challenging computational tasks in machine learning community due to the increased amounts of natural language text documents available in the electronic forms. In this process, feature selection (FS) is an essential phase because thousands of possible feature sets may be considered in text classification. This paper proposes an enhanced binary grey wolf optimizer (GWO) within a wrapper FS approach to tackle Arabic text classification problems. The proposed binary GWO is utilized to play the role of a wrapper-based feature selection technique. The performance of the proposed method using different learning models, including decision trees, K-nearest neighbour, Naive Bayes, and SVM classifiers, are investigated. Three Arabic public datasets, namely Alwatan, Akhbar-Alkhaleej, and Al-jazeera-News, are utilized to evaluate the efficacy of different BGWO-based wrapper methods. Results and analysis show that SVM-based feature selection technique with the proposed binary GWO optimizer with elite-based crossover scheme has enhanced efficacy in dealing with Arabic text classification problems compared to other peers. [ABSTRACT FROM AUTHOR]

Nowadays the incredibly advanced developments in information technologies have led to exponential growth in the datasets with respect to both the dimensionality and the sample size. This trend can be easily illustrated in popular online data repositories (e.g., UCI machine learning repository). The more growth in the datasets, the more challenging the data management becomes. This is because such datasets usually comprise a high level of noise as well as the necessary information. Therefore, the elimination of noisy features in the datasets is an important task to discover meaningful knowledge. Although a large number of feature selection approaches have been proposed in the literature to deal with noisy features, the need for the studies based on feature selection has not come to an end. In this paper, we propose differential evolution-based feature selection approaches wrapped around the principles of fuzzy rough set theory. In contrast to well-known feature selection criteria such as standard mutual information, standard rough set and probabilistic rough set, our approaches can also deal with real-valued variables without the requirement of discretization. Moreover, the feature subsets selected by our approaches can profoundly improve the classification performance compared to the recent particle swarm optimization approaches based on probabilistic rough set and the state-of-the-art filter approaches. [ABSTRACT FROM AUTHOR]

Most of the text in the questions of community question–answering systems does not consist of a definite mechanism for the restriction of inappropriate and insincere content. A given piece of text can be insincere if it asserts false claims or assumes something which is debatable or has a non-neutral or exaggerated tone about an individual or a group. In this paper, we propose a pipeline called Deep Refinement which utilizes some of the state-of-the-art methods for information retrieval from highly sparse data such as capsule network and attention mechanism. We have applied the Deep Refinement pipeline to classify the text primarily into two categories, namely sincere and insincere. Our novel approach 'Deep Refinement' provides a system for the classification of such questions in order to ensure enhanced monitoring and information quality. The database used to understand the real concept of what actually makes up sincere and insincere includes quora insincere question dataset. Our proposed question classification method outperformed previously used text classification methods, as evident from the F1 score of 0.978. [ABSTRACT FROM AUTHOR]

With advanced data analysis techniques, efforts for more accurate decision support systems for disease prediction are on the rise. According to the World Health Organization, diabetes-related illnesses and mortalities are on the rise. Hence, early diagnosis is particularly important. In this paper, we present a framework, Auto-MeDiSine, that comprises an automated version of enhanced class outlier detection using a distance-based algorithm (AutoECODB), combined with an ensemble of automatic multilayer perceptron (AutoMLP). AutoECODB is built upon ECODB by automating the tuning of parameters to optimize outlier detection process. AutoECODB cleanses the dataset by removing outliers. Preprocessed dataset is then used to train a prediction model using an ensemble of AutoMLPs. A set of experiments is performed on publicly available Pima Indian Diabetes Dataset as follows: (1) Auto-MeDiSine is compared with other state-of-the-art methods reported in the literature where Auto-MeDiSine realized an accuracy of 88.7%; (2) AutoMLP is compared with other learners including individual (focusing on neural network-based learners) and ensemble learners; and (3) AutoECODB is compared with other preprocessing methods. Furthermore, in order to validate the generality of the framework, Auto-MeDiSine is tested on another publicly available BioStat Diabetes Dataset where it outperforms the existing reported results, reaching an accuracy of 97.1%. [ABSTRACT FROM AUTHOR]

Infertility affects one out of seven couples around the world. Therefore, the best possible management of the in vitro fertilization (IVF) treatment and patient advice is crucial for both patients and medical practitioners. The ultimate concern of the patients is the success of an IVF procedure, which depends on a number of influencing attributes. Without any automated tool, it is hard for the practitioners to assess any influencing trend of the attributes and factors that might lead to a successful IVF pregnancy. This paper proposes a hill climbing feature (attribute) selection algorithm coupled with automated classification using machine learning techniques with the aim to analyze and predict IVF pregnancy in greater accuracy. Using 25 attributes, we assessed the prediction ability of IVF pregnancy success for five different machine learning models, namely multilayer perceptron (MLP), support vector machines (SVM), C4.5, classification and regression trees (CART) and random forest (RF). The prediction ability was measured in terms of widely used performance metrics, namely accuracy rate, F-measure and AUC. Feature selection algorithm reduced the number of most influential attributes to nineteen for MLP, sixteen for RF, seventeen for SVM, twelve for C4.5 and eight for CART. Overall, the most influential attributes identified are: 'age', 'indication' of fertility factor, 'Antral Follicle Counts (AFC)', 'NbreM2', 'method of sperm collection', 'Chamotte', 'Fertilization rate in vitro', 'Follicles on day 14' and 'Embryo transfer day.' The machine learning models trained with the selected set of features significantly improved the prediction accuracy of IVF pregnancy success to a level considerably higher than those reported in the current literature. [ABSTRACT FROM AUTHOR]

This paper presents a prototype to classify stroke that combines text mining tools and machine learning algorithms. Machine learning can be portrayed as a significant tracker in areas like surveillance, medicine, data management with the aid of suitably trained machine learning algorithms. Data mining techniques applied in this work give an overall review about the tracking of information with respect to semantic as well as syntactic perspectives. The proposed idea is to mine patients' symptoms from the case sheets and train the system with the acquired data. In the data collection phase, the case sheets of 507 patients were collected from Sugam Multispecialty Hospital, Kumbakonam, Tamil Nadu, India. Next, the case sheets were mined using tagging and maximum entropy methodologies, and the proposed stemmer extracts the common and unique set of attributes to classify the strokes. Then, the processed data were fed into various machine learning algorithms such as artificial neural networks, support vector machine, boosting and bagging and random forests. Among these algorithms, artificial neural networks trained with a stochastic gradient descent algorithm outperformed the other algorithms with a higher classification accuracy of 95% and a smaller standard deviation of 14.69. [ABSTRACT FROM AUTHOR]

Semi-supervised classification is a hot topic in pattern recognition and machine learning. However, in presence of heavy noise and outliers, the unlabeled training data could be very challenging or even misleading for the semi-supervised classifier. In this paper, we propose a novel structure regularized self-paced learning method for semi-supervised classification problems, which can efficiently learn partially labeled training data sequentially from the simple to the complex ones. The proposed formulation consists of three components: a cost function defined by a mixture of losses, a functional complexity regularizer, and a self-paced regularizer; and the corresponding optimization algorithm involves three iterative steps: classifier updating, sample importance calculating, and pseudo-labeling. In the proposed method, the cost function for classifier updating and sample importance calculating is defined as a combination of the label fitting loss and manifold smoothness loss. Then, the importance of the pseudo-labeled and unlabeled samples is adaptively calculated by the novel cost. Unlabeled samples with high importance values are pseudo-labeled with their current predictions. In this way, labels are efficiently propagated from the labeled samples to the unlabeled ones in the robust self-paced manner. Experimental results on several benchmark data sets are provided to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Support vector data description (SVDD), an efficient one-class classification method, captures the spherically shaped boundary around the same class data and achieves classification for setting the boundary related to support vectors (SVs). As SVDD constructs an irregular hypersphere in high-dimensional space, it is unreasonable to keep the classification boundary a constant value. When the classification dataset is complicated, constant classification boundary will decrease the accuracy of classification. In this paper, we present a dynamic hypersphere SVDD (DH-SVDD) without describing boundary for one-class classification. In training process, important SVs of training dataset describe the static hypersphere. In testing process, dynamic hypersphere is described according to the new important SVs of the testing sample and training dataset. If there is a significant change of hypersphere structure, it means the new sample is an outlier. In this method, without any classification boundary, it can complete one-class classification with fully considering the related information of new sample and historical dataset. Thus, it can significantly improve the one-class classification accuracy of SVDD in complex datasets. Comparison is conducted among the proposed DH-SVDD, K-chart SVDD, Max limit SVDD and Validation limit SVDD. The effectiveness of the proposed method is also verified by the experimental UCI datasets. [ABSTRACT FROM AUTHOR]

This paper proposes a new spheres-based support vector machine (SSVM) for binary data classification. The proposed SSVM is formulated by clustering the training points according to the similarity between classes, i.e., it constructs two spheres simultaneously by solving a single optimization programming problem, in which each point is as far as possible away from the sphere center of opposite class and its projection value on the directed line segment between the two centers is as far as possible not larger than the corresponding radius. This SSVM has a perfect geometric interpretation for its dual problem. By considering the characteristics of the dual optimization problem of SSVM, an efficient learning algorithm for SSVM, which can be easily extended to other SVM-type classifiers, based on the gradient descent and the clipping strategy is further presented. Computational results on several synthetic as well as benchmark datasets indicate the significant advantages of the SSVM classifier in the computational cost and test accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes and optimizes a two-term cost function consisting of a sparseness term and a generalized v-fold cross-validation term by a new adaptive particle swarm optimization (APSO). APSO updates its parameters adaptively based on a dynamic feedback from the success rate of the each particle’s personal best. Since the proposed cost function is based on the choosing fewer numbers of support vectors, the complexity of SVM model is decreased while the accuracy remains in an acceptable range. Therefore, the testing time decreases and makes SVM more applicable for practical applications in real data sets. A comparative study on data sets of UCI database is performed between the proposed cost function and conventional cost function to demonstrate the effectiveness of the proposed cost function. [ABSTRACT FROM AUTHOR]

In recent years, sparse coding via dictionary learning has been widely used in many applications for exploiting sparsity patterns of data. For classification, useful sparsity patterns should have discrimination, which cannot be well achieved by standard sparse coding techniques. In this paper, we investigate structured sparse coding for obtaining discriminative class-specific group sparsity patterns in the context of classification. A structured dictionary learning approach for sparse coding is proposed by considering the ℓ2,0 norm on each class of data. An efficient numerical algorithm with global convergence is developed for solving the related challenging ℓ2,0 minimization problem. The learned dictionary is decomposed into class-specific dictionaries for the classification that is done according to the minimum reconstruction error among all the classes. For evaluation, the proposed method was applied to classifying both the synthetic data and real-world data. The experiments show the competitive performance of the proposed method in comparison with several existing discriminative sparse coding methods. [ABSTRACT FROM AUTHOR]

Recently, a novel learning algorithm for single-hidden-layer feedforward neural networks (SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The essence of ELM is that the learning parameters of hidden nodes, including input weights and biases, are randomly assigned and need not be tuned while the output weights can be analytically determined by the simple generalized inverse operation. The only parameter needed to be defined is the number of hidden nodes. Compared with other traditional learning algorithms for SLFNs, ELM provides extremely faster learning speed, better generalization performance and with least human intervention. This paper firstly introduces a brief review of ELM, describing the principle and algorithm of ELM. Then, we put emphasis on the improved methods or the typical variants of ELM, especially on incremental ELM, pruning ELM, error-minimized ELM, two-stage ELM, online sequential ELM, evolutionary ELM, voting-based ELM, ordinal ELM, fully complex ELM, and symmetric ELM. Next, the paper summarized the applications of ELM on classification, regression, function approximation, pattern recognition, forecasting and diagnosis, and so on. In the last, the paper discussed several open issues of ELM, which may be worthy of exploring in the future. [ABSTRACT FROM AUTHOR]

At the chromosomal level of evolution, recombination is a major factor for genetic variations. However, recombination does not occur with equal frequency at various regions of genome. The recombination has the tendency to occur at specific region with higher frequency and with low frequency at other regions, and former regions are named as hot recombination regions whereas later are called cold regions for recombination. In this paper, we have developed supervised machine learning-based models using artificial neural network, support vector machine and Naïve Bayes for efficient and effective classification of such hot and cold recombination regions based on the nucleotide composition of sequences. All models were validated and tested using tenfold cross-validation. Furthermore, neural network model was validated using leave one out and random sampling techniques in addition to tenfold cross-validation. Moreover, models were evaluated using receiver-operating curve. Our results indicate that artificial neural network achieves the best result. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel unsupervised learning approach for spatio-temporal pattern classification. We use a spike timing neural network with axonal conductance delays to learn the structure of spatio-temporal patterns from a small set of training samples and then use this network for classifying unseen patterns. The method also enables early classification of patterns, before they are completely observed. To transform a spatio-temporal pattern into a suitable form for the spiking network, we create a mapping process that transforms it into a spike train that can be used to train the network through spike-timing-dependent plasticity. Based on the trained network, we build models of the training samples as strings of characters in which each character represents a set of neurons that fire at a particular time step, in response to the pattern. For classification, we compute the longest common subsequence between the model strings corresponding to the input and training samples and choose the class of the training sample with highest similarity. This method is evaluated on a handwritten digits dataset with spatio-temporal information. We show that this method is robustly detecting the correct class early on. Comparison with one unsupervised and eight other supervised approaches shows that our proposed approach could compete or has better performance rather than all of them. Further analysis show that even for misclassified samples, our system can detect the correct class among the top three class labels. [ABSTRACT FROM AUTHOR]

In order to overcome the disadvantage of the traditional algorithm for SLFN (single-hidden layer feedforward neural network), an improved algorithm for SLFN, called extreme learning machine (ELM), is proposed by Huang et al. However, ELM is sensitive to the neuron number in hidden layer and its selection is a difficult-to-solve problem. In this paper, a self-adaptive mechanism is introduced into the ELM. Herein, a new variant of ELM, called self-adaptive extreme learning machine (SaELM), is proposed. SaELM is a self-adaptive learning algorithm that can always select the best neuron number in hidden layer to form the neural networks. There is no need to adjust any parameters in the training process. In order to prove the performance of the SaELM, it is used to solve the Italian wine and iris classification problems. Through the comparisons between SaELM and the traditional back propagation, basic ELM and general regression neural network, the results have proven that SaELM has a faster learning speed and better generalization performance when solving the classification problem. [ABSTRACT FROM AUTHOR]

Real-time transient stability status prediction (RTSSP) is very important to maintain the safety and stability of electrical power systems, where any unstable contingency will be likely to cause large-scale blackout. Most of machine learning methods used for RTSSP attempt to attain a low classification error, which implies that the misclassification costs of different categories are the same. However, misclassifying an unstable case as stable one usually leads to much higher costs than misclassifying a stable case as unstable one. In this paper, a new RTSSP method based on cost-sensitive extreme learning machine (CELM) is proposed, which recognizes the RTSSP as a cost-sensitive classification problem. The CELM is constructed pursuing the minimum misclassification costs, and its detailed implementation procedures for RSSTP are also researched in this work. The proposed method is implemented on the New England 39-bus electrical power system. Compared with three cost-blind methods (ELM, SVM and DT) and two cost-sensitive methods (cost-sensitive DT, cost-sensitive SVM), the simulation results have proved that the lower total misclassification costs and false dismissal rate with low computational complexity can be achieved by the proposed method, which meets the demands for the computation speed and the reliability of RTSSP. [ABSTRACT FROM AUTHOR]

Due to the significant efficiency and simple implementation, extreme learning machine (ELM) algorithms enjoy much attention in regression and classification applications recently. Many efforts have been paid to enhance the performance of ELM from both methodology (ELM training strategies) and structure (incremental or pruned ELMs) perspectives. In this paper, a local coupled extreme learning machine (LC-ELM) algorithm is presented. By assigning an address to each hidden node in the input space, LC-ELM introduces a decoupler framework to ELM in order to reduce the complexity of the weight searching space. The activated degree of a hidden node is measured by the membership degree of the similarity between the associated address and the given input. Experimental results confirm that the proposed approach works effectively and generally outperforms the original ELM in both regression and classification applications. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of extreme learning machine (ELM)-based microarray data classification. Different from the traditional classification problem, the goal in this case is not just to predict the class labels for the unseen samples, but to make clear what lead to the results, i.e., the genes involving with a specific disease. This is especially significant for biologists, since they need to decipher the causes of disease. As a black-box method, ELM could not measure up to the task by itself. In this work, we propose a diversified sequence feature selection-based framework to address the problem. In this framework, (1) a sequence model, EWave, is introduced to ensure the structural ordering information among genes exploitable; (2) a concept of irreducible sequence is proposed, where the genes work as an orderly whole to keep high confidence with a specific class and any reduction in the genes decreases the confidence much. An efficient sequence mining algorithm together with some effective pruning rules is developed to mine such sequences; and (3) we study how to extract a set of diversified sequence features as the representative of all mined results. The problem is proved to be NP-hard. A greedy algorithm is presented to approximate the optimal solution. Experimental results show that the proposed approach significantly improves the efficiency and the effectiveness of ELM w.r.t some widely used feature selection techniques. [ABSTRACT FROM AUTHOR]

PU classification problem ('P' stands for positive, 'U' stands for unlabeled), which is defined as the training set consists of a collection of positive and unlabeled examples, has become a research hot spot recently. In this paper, we design a new classification algorithm to solve the PU problem: biased twin support vector machine (B-TWSVM). In B-TWSVM, two nonparallel hyperplanes are constructed such that the positive examples can be classified correctly, and the number of unlabeled examples classified as positive is minimized. Moreover, considering that the unlabeled set also contains positive data, different penalty parameters for positive and negative data are allowed in B-TWSVM. Experimental results demonstrate that our method outperforms the state-of-the-art methods in most cases. [ABSTRACT FROM AUTHOR]

Feature selection has been widely discussed as an important preprocessing step in machine learning and data mining. In this paper, a new feature selection evaluation criterion based on low-loss learning vector quantization (LVQ) classification is proposed. Based on the evaluation criterion, a feature selection algorithm that optimizes the hypothesis margin of LVQ classification through minimizing its loss function is presented. Some experiments that are compared with well-known SVM-RFE and Relief are carried out on 4 UCI data sets using Naive Bayes and RBF Network classifier. Experimental results show that new algorithm achieves similar or even higher performance than Relief on all training data and has better or comparable performance than SVM-RFE. [ABSTRACT FROM AUTHOR]

This paper proposes a classifier named ensemble of polyharmonic extreme learning machine, whose part weights are randomly assigned, and it is harmonic between the feedforward neural network and polynomial. The proposed classifier provides a method for human face recognition integrating fast discrete curvelet transform (FDCT) with 2-dimension principal component analysis (2DPCA). FDCT is taken to be a feature extractor to obtain facial features, and then these features are dimensionality reduced by 2DPCA to decrease the computational complexity before they are input to the classifier. Comparison experiments of the proposed method with some other state-of-the-art approaches for human face recognition have been carried out on five well-known face databases, and the experimental results show that the proposed method can achieve higher recognition rate. [ABSTRACT FROM AUTHOR]