Search

• A new graph-based data classification method is presented. • The method can be applied to both semi-supervised and unsupervised learning tasks. • Some of its properties allow it to perform well with small labeled training sets. • It is unconditionally stable, incorporates class size information and efficient. • Experiments are performed on classification of 3D sensory data such as point clouds. Data classification, where the goal is to divide data into predefined classes, is a fundamental problem in machine learning with many applications, including the classification of 3D sensory data. In this paper, we present a data classification method which can be applied to both semi-supervised and unsupervised learning tasks. The algorithm is derived by unifying complementary region-based and edge-based approaches; a gradient flow of the optimization energy is performed using modified auction dynamics. In addition to being unconditionally stable and efficient, the method is equipped with several properties allowing it to perform accurately even with small labeled training sets, often with considerably fewer labeled training elements compared to competing methods; this is an important advantage due to the scarcity of labeled training data. Some of the properties are: the embedding of data into a weighted similarity graph, the in-depth construction of the weights using, e.g., geometric information, the use of a combination of region-based and edge-based techniques, the incorporation of class size information and integration of random fluctuations. The effectiveness of the method is demonstrated by experiments on classification of 3D point clouds; the algorithm classifies a point cloud of more than a million points in 1–2 min. [ABSTRACT FROM AUTHOR]

Accurate and efficient recognition of Parkinson's disease is one of the prominent issues in the field of healthcare. To address this problem, different methods have been proposed in the literature. However, existing methods are lacking in accurately recognizing the Parkinson's disease and suffer from efficiency problems. To overcome these problems faced by existing models, this paper presents a machine-learning-based model for Parkinson's disease recognition. Specifically, a hybrid feature selection algorithm has been designed by integrating the Relief and ant-colony optimization algorithms to select relevant features for training the model. Moreover, the support vector machine has been trained and tested on the selected features to achieve optimal classification accuracy. Additionally, the K-fold cross-validation technique has been employed for the optimal hyper-parameters value evaluation of the model.The experimental results on a real-world dataset, i.e., Parkinson's disease dataset is revealed that the proposed system outperforms baseline competitors by accurately recognizing the Parkinson's disease and achieving 99.50% accuracy on the selected features. Due to high performance is achieved our proposed method, we are highly recommended for the recognition of PD. [ABSTRACT FROM AUTHOR]

This paper proposes a new content recommendation system which combines the newly proposed embedded feature selection method and the new Fuzzy Temporal Logic based Decision Tree incorporated Convolutional Neural Network classifier. The newly proposed embedded feature selection called Fuzzy Decision Tree and Weighted Gini-Index based Feature Selection Algorithm (FDTWGI-FSA) that contains the existing incorporated the Fuzzy Decision Tree (FDT) and the Weighted Gini-index based Feature Selection Algorithm (WGIFSA) for getting optimized feature subset. Moreover, an enhanced CNN and Fuzzy Temporal Decision Tree for performing the deep learning process which is able to identify the exact e-content from the huge volume of data with the help of the recommended features by the proposed embedded feature selection method. The exact e-content can be identified after performing the five-layer network structure for extracting the relevant features and it also can be classified by applying the Fuzzy Temporal Decision Tree for the e-learners. Finally, the proposed content recommendation system provides exact content to the e-learners according to their level of understanding and it also satisfies them by providing the exact high level contents. The experiments have been conducted for evaluating the proposed content recommendation system and compared with the existing classifier including the standard CNN. [ABSTRACT FROM AUTHOR]

Dictionary learning has emerged as a powerful tool for a range of image processing applications and a proper dictionary always plays a key issue to the final achievable performance. In this paper, a class-oriented discriminative dictionary learning (CODDL) method is presented for image classification applications. It takes a comprehensive consideration of multiple optimization objectives, emphasizing class discrimination of both dictionary atoms and representation coefficients. The atoms of the learned dictionary should be grouped into class level sub-dictionaries. Meanwhile, the sparse representation coefficients of an input sample should be concentrated on the sub-dictionary of the class it belongs to. Then, based on the learned class-oriented discriminative dictionary, the structured representation coefficients can thus be used for image classification with a simple and efficient classification scheme. The superior performance of the proposed algorithm is demonstrated through extensive experiments. [ABSTRACT FROM AUTHOR]

This paper consists of two parts. In the first part, organizational approaches to manage the development and implementation of complex projects on the creation of innovative products and technologies for energy-saving railway transport are proposed. Some methods for estimating and classifying projects by the degree of implementability and effectiveness are considered. A resource planning model and a corresponding algorithm for a set of projects are presented. [ABSTRACT FROM AUTHOR]

The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective analysis of such large datasets. While supervised classification methods have been developed for this purpose in recent years, their ability to generalize is limited by the representativeness of their labeled training datasets, which are affected by the subjective judgment of the expert and require significant manual effort to derive. An alternative is unsupervised classification, which seeks to divide a dataset into distinct classes without expert-provided labels. In this paper, we introduce an unsupervised classification scheme based on a generative adversarial network (GAN) that learns to extract the key features from the snowflake images. Each image is then associated with a distribution of points in the feature space, and these distributions are used as the basis of K -medoids classification and hierarchical clustering. We found that the classification scheme is able to separate the dataset into distinct classes, each characterized by a particular size, shape and texture of the snowflake image, providing signatures of the microphysical properties of the snowflakes. This finding is supported by a comparison of the results to an existing supervised scheme. Although training the GAN is computationally intensive, the classification process proceeds directly from images to classes with minimal human intervention and therefore can be repeated for other MASC datasets with minor manual effort. As the algorithm is not specific to snowflakes, we also expect this approach to be relevant to other applications. [ABSTRACT FROM AUTHOR]

In this paper, we describe the equivalence classes of simple arcs between the two punctures on a 2-punctured torus Σ 1 , 2 up to isotopy by using the given four generators g 1 , g 2 , g 3 and g 4 . Actually, we show that a class of simple arcs is represented by an ordered sequence of four integers. Also, we introduce an algorithm to check whether or not an ordered sequence of four integers represents a class of simple arcs in Σ 1 , 2. We want to point out that this result classifies the (1 , 1) -positions. [ABSTRACT FROM AUTHOR]

This paper designs a novel classification hardware framework based on neural network (NN). It utilizes COordinate Rotation DIgital Computer (CORDIC) algorithm to implement the activation function of NNs. The training was performed through software using an error back-propagation algorithm (EBPA) implemented in C++, then the final weights were loaded to the implemented hardware framework to perform classification. The hardware framework is developed in Xilinx 9.2i environment using VHDL as programming languages. Classification tests are performed on benchmark datasets obtained from UCI machine learning data repository. The results are compared with competitive classification approaches by considering the same datasets. Extensive analysis reveals that the proposed hardware framework provides more efficient results as compared to the existing classifiers. [ABSTRACT FROM AUTHOR]

Line simplification is one of the most important parts of automatic map generalization. Aiming at the problem that the effect of the current line simplification methods is too dependent on the artificial setting of algorithm parameters and thresholds, and lacking the adaptive learning ability for different simplification environments, this paper considers the line simplification as a binary classification problem about the selection or deletion of the simplification unit. From the perspective of case-based studying, a new line simplification method based on support vector machine (SVM) is proposed. Firstly, point and bend are taken as the simplification unit, the simplification case is automatically obtained from the simplification results of experts, and then the feature description items are extracted as the attribute space of the case sample. The line simplification classifier is trained by SVM. Finally, the SVM classifier is used to classify the simplification units of the new line elements into selection and deletion, so that the line simplification is realized. The experimental results show that this method can well reduce the simplification intention of the experts by learning the simplification case, with a high classification accuracy for the selection and deletion of simplification units, and can adaptively complete the line simplification. [ABSTRACT FROM AUTHOR]

In order to solve the problem of classification of large-scale intrusion data, this paper proposed lightGBM binary tree algorithm based on stacked sparse autoencoder. Firstly, it divided the category labels into five categories and constructed into binary tree structures. Then solved the imbalance of data distribution by the upper sampling method, the above processing could separate the large-scale data, so that they could be trained separately. Next, it used the sparse autoencoder network to reduce the feature dimension. Using this method could ensure that time of dimension reduction could save on the basis of extracting deeper features from the original data. Finally, it used the lightGBM ensemble algorithm to classify. And compared to other models, using the lightGBM model could save training time while ensuring classification performance. It used the NSL-KDD dataset to measure the accuracy, precision, recall. And comprehensive evaluation index F1 of the proposed method, which reached an average of 87. 42%, 98. 20% and 91. 31% in five classification, respectively. It is superior to the comparison algorithm and obviously saves the calculation time . [ABSTRACT FROM AUTHOR]

Swarm-based algorithm is best suitable when it can perform smooth balance between the exploration and exploitation as well as faster convergence by successfully avoiding local optima entrapment. At recent time, salp swarm algorithm (SSA) is developed as a nature-inspired swarm-based algorithm. It can solve continuous, nonlinear and complex in nature day-to-day life optimization problems. Like many other optimization algorithms, SSA suffers with the problem of local stagnation. This paper introduces an improved version of the SSA, which improves the performance of the existing SSA by using space transformation search (STS). The proposed algorithm is termed as STS-SSA. The STS-SSA enhances the exploration and exploitation capability in the search space and successfully avoids local optima entrapment. The STS-SSA is evaluated by considering the IEEE CEC 2017 standard benchmark function set. The efficiency and robustness of the proposed STS-SSA are measured using performance metrics, convergence analysis and statistical significance. A demonstration is given as an application of the proposed algorithm for solving a real-life problem. For this purpose, the multi-layer feed-forward network is trained using the proposed STS-SSA. The experimental results demonstrate that the developed STS-SSA can be used for solving optimization problems effectively. [ABSTRACT FROM AUTHOR]

Recently, sparse representation-based classification (SRC) has been widely studied and has produced state-of-the-art results in various classification tasks. Learning useful and computationally convenient representations from complex redundant and highly variable visual data is crucial for the success of SRC. However, how to find the best feature representation to work with SRC remains an open question. In this paper, we present a novel discriminative projection learning approach with the objective of seeking a projection matrix such that the learned low-dimensional representation can fit SRC well and that it has well discriminant ability. More specifically, we formulate the learning algorithm as a bilevel optimization problem, where the optimization includes an $\ell _{1}$ -norm minimization problem in its constraints. Through the bilevel optimization model, the relationship between sparse representation and the desired feature projection can be explicitly exploited during the learning process. Therefore, SRC can achieve a better performance in the transformed subspace. The optimization model can be solved by using a stochastic gradient ascent algorithm, and the desired gradient is computed using implicit differentiation. Furthermore, our method can be easily extended to learn a dictionary. The extensive experimental results on a series of benchmark databases show that our method outperforms many state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Summary: This paper presents a protection method for microgrids by data mining of voltage disturbances. The occurrence of a fault on the system is associated with a sudden voltage depression, which is fast detected by the adaptive cumulative sum (ACUSUM) algorithm. There are other operational (no‐fault) events causing voltage depression such as motor starting, transformer energizing, and capacitor or heavy load switching. In order to discriminate between the fault and no‐fault events, one cycle of the voltage waveform is preprocessed by the short‐time Fourier transform (STFT) to extract and construct effective features of the disturbance. The features are then used in the decision trees (DTs) for the discrimination. The proposed protection method is tested for fault or no‐fault conditions of grid‐connected or islanded mode of the microgrid operation, as well as radial or meshed topology. The proposed method also identifies the fault type and faulted phase(s) for selective phase tripping. The immunity of the method against different noise levels is investigated. It is shown by the simulation study that by using only two features for symmetrical events and six features for asymmetrical events, any fault can be detected accurately. [ABSTRACT FROM AUTHOR]

Fuzzy c-means (FCM) is one of the best-known clustering methods to organize the wide variety of datasets automatically and acquire accurate classification, but it has a tendency to fall into local minima. For overcoming these weaknesses, some methods that hybridize PSO and FCM for clustering have been proposed in the literature, and it is demonstrated that these hybrid methods have an improved accuracy over traditional partition clustering approaches, whereas PSO-based clustering methods have poor execution time in comparison to partitional clustering techniques, and the current PSO algorithms require tuning a range of parameters before they are able to find good solutions. Therefore, this paper introduces a hybrid method for fuzzy clustering, named FCM-ELPSO, which aim to deal with these shortcomings. It combines FCM with an improved version of PSO, called ELPSO, which adopts a new enhanced logarithmic inertia weight strategy to provide better balance between exploration and exploitation. This new hybrid method uses PBM(F) index and the objective function value as cluster validity indexes to evaluate the clustering effect. To verify the effectiveness of the algorithm, two types of experiments are performed, including PSO clustering and hybrid clustering. Experiments show that the proposed approach significantly improves convergence speed and the clustering effect. [ABSTRACT FROM AUTHOR]

G-Protein-Coupled Receptors (GPCR) are the large family of protein membrane; and until now some of them still remain orphans. Predicting GPCR functions is a challenging task, it depends closely to their classification, which requires a digital representation of each protein chain as an attribute vector. A major problem of GPCR databases is their great number of features which can produce combinatorial explosion and increase the complexity of classification algorithms. Feature selection techniques are used to deal with this problem by minimizing features space dimension, and keeping the most relevant ones. In this paper, we propose to use the BAT algorithm for extracting the pertinent features and to improve the classification results. We compared the results obtained by our system with two other bio-inspired algorithms, Evolutionary Algorithm and PSO search. Metrics quality measures used for comparison are Error Rate, Accuracy, MCC and F -measure. Experimental results indicate that our system is more efficient. [ABSTRACT FROM AUTHOR]

Aiming at the problems of low recall rate and poor running efficiency caused by traditional abnormal accounts detecting algorithms in non-balanced social network datasets, the paper extracted user content, behavior, attributes, and relationship features from social network data sets, selected features using gradient-enhanced ensemble classifier XGBoost algorithm, established classification model,constructed unbalanced data sets and realized the identification of three types of spam accounts. Experimental results show that it improves the recall rate and the F1 value in identification of three types of abnormal users effectively by XGBoost algorithm in binary classification and multiple classification tasks both in the balanced and unbalanced dataset in comparison with the traditional classification methods such as random forest. And with few features selected by XGBoost, the classification algorithm can get the same effect as with all features of samples, which proves the effectiveness of this method. [ABSTRACT FROM AUTHOR]

In recent years, the sources localization has noticed an increase in research conducted on the problem of mixed far-field sources (FFSs) and near-field sources (NFSs). The main assumption of the existing researches is that the signals should be uncorrelated. Therefore, they cannot be used for multipath environments. The present paper provides a method called components separation algorithm (CSA) for the localization of multiple mixed FFSs and NFSs, including uncorrelated, lowly correlated and coherent signals. Firstly, by constructing one special cumulant matrix, and using a MUSIC-based technique, the noncoherent DOA vector (NDOAV) is extracted. By constructing another special cumulant matrix, and with respect to NDOAV, an estimate of the range, as well as a signal classification is obtained for noncoherent sources. Then, by estimating their kurtosis, the noncoherent component and consequently the coherent one of the second cumulant matrix is obtained. Finally, by introducing a novel approach based on squaring, projection, spatial smoothing, array interpolation transform and coherent component restoring, the parameters of coherent signals in each coherent group are estimated separately. The CSA prevents severe loss of the aperture. Furthermore, it does not require any pairing. The simulation results validate its satisfactory performance in terms of estimation accuracy, resolution, computational complexity, reasonable classification, and also its robustness against lowly correlated sources. [ABSTRACT FROM AUTHOR]

Classification algorithms' performance could be enhanced by selecting many representative points to be included in the training sample. In this paper, a new border and rare biased sampling (BRBS) scheme is proposed by assigning each point in the dataset an importance factor. The importance factor of border points and rare points (i.e. points belong to rare classes) is higher than other points. Then the points are selected to be in the training sample depending on these factors. Including these points in the training sample enhances classifiers experience. The results of experiments on 10 UCI machine learning repository datasets prove that the BRBS algorithm outperforms many sampling algorithms and enhanced the performance of several classification algorithms by about 8%. BRBS is proposed to be easy to configure, covering all points space, and generate a unique samples every time it is executed. [ABSTRACT FROM AUTHOR]

Aiming at the challenges faced by the existing big data-oriented computing framework in the study of extensible machine learning, this paper proposed a distributed naive Bayesian text classification method based on MapReduce and Apache Spark framework. This method explored the Bayesian network classifier by studying the adaptability of MapReduce and Apache Spark frameworks, and studied the existing computing framework for big data. First, it divided the training sample data set into m classes based on the naive Bayes text classification model. In the training phase, it used the output of the previous MapReduce as the input of the next MapReduce, and used four MapReduce jobs to derive the model. This design process made full use of the parallel advantages of MapReduce. Finally, when the classifier was tested, it obtained the value of the class label which the maximum value belonged. Experiments in the Newgroup's dataset show the proposed method achieves more than 99% of the results on all five types of news data sets, and is all higher than the comparison algorithms, which prove the accuracy of the method. [ABSTRACT FROM AUTHOR]

Link prediction in a graph is the problem of detecting the missing links or the ones that would be formed in the near future. Using a graph representation of the data, we can convert the problem of classification to the problem of link prediction which aims at finding the missing links between the unlabeled data (unlabeled nodes) and their classes. To our knowledge, despite the fact that numerous algorithms use the graph representation of the data for classification, none are using link prediction as the heart of their classifying procedure. In this work, we propose a novel algorithm called CULP (C lassification U sing L ink P rediction) which uses a new structure namely Label Embedded Graph or LEG and a link predictor to find the class of the unlabeled data. Different link predictors along with Compatibility Score - a new link predictor we proposed that is designed specifically for our settings - has been used and showed promising results for classifying different datasets. This paper further improved CULP by designing an extension called CULM which uses a majority vote (hence the M in the acronym) procedure with weights proportional to the predictions' confidences to use the predictive power of multiple link predictors and also exploits the low level features of the data. Extensive experimental evaluations shows that both CULP and CULM are highly accurate and competitive with the cutting edge graph classifiers and general classifiers. [ABSTRACT FROM AUTHOR]

The shortage of training samples remains one of the main obstacles in applying the neural networks to the hyperspectral images classification. To fuse the spatial and spectral information, pixel patches are often utilized to train a model, which may further aggregate this problem. In the existing works, an artificial neural network (ANN) model supervised by centerloss (ANNC) was introduced. Training merely with spectral information, the ANNC yields discriminative spectral features suitable for the subsequent classification tasks. In this paper, we propose a novel convolutional neural network (CNN)-based spatial feature fusion (CSFF) algorithm, which allows a smart integration of spatial information to the spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based discriminant model is introduced to estimate whether two pixels belong to the same class. At the testing stage, by applying the discriminant model to the pixel pairs generated by a test pixel and each of its neighbors, the local structure is estimated and represented as a customized convolutional kernel. The spectral–spatial feature is generated by a convolutional operation between the estimated kernel and the corresponding spectral features within a local region. The final label is determined by classifying the resulting spectral–spatial feature. Without increasing the number of training samples or involving pixel patches at the training stage, the CSFF framework achieves the state of the art by declining 20%–50% classification failures in experiments on three well-known hyperspectral images. [ABSTRACT FROM AUTHOR]

In order to improve the classification effect of the gene expression profile with high dimension and small sample, this paper proposes an improved rotation forest algorithm. Firstly, the information index to classification algorithm (IIC) can't deal with multi-class problems, and then an improved IIC algorithm is proposed to achieve the attribute reduction so that the noise genes are eliminated and the dimensions of feature space are reduced. Secondly, the rotation forest algorithm was improved from the two aspects of improving diversity and accuracy of the base classifiers to classify gene expression profiles. The simulation experimental results on the benchmark gene expression profile datasets show that our proposed algorithm is better than rotation forest algorithm in classification accuracy, stability and running time. [ABSTRACT FROM AUTHOR]

Big web data from sources including online news and Twitter are good resources for investigating deep learning. However, collected news articles and tweets almost certainly contain data unnecessary for learning, and this disturbs accurate learning. This paper explores the performance of word2vec Convolutional Neural Networks (CNNs) to classify news articles and tweets into related and unrelated ones. Using two word embedding algorithms of word2vec, Continuous Bag-of-Word (CBOW) and Skip-gram, we constructed CNN with the CBOW model and CNN with the Skip-gram model. We measured the classification accuracy of CNN with CBOW, CNN with Skip-gram, and CNN without word2vec models for real news articles and tweets. The experimental results indicated that word2vec significantly improved the accuracy of the classification model. The accuracy of the CBOW model was higher and more stable when compared to that of the Skip-gram model. The CBOW model exhibited better performance on news articles, and the Skip-gram model exhibited better performance on tweets. Specifically, CNN with word2vec models was more effective on news articles when compared to that on tweets because news articles are typically more uniform when compared to tweets. [ABSTRACT FROM AUTHOR]

Learning algorithms based on probability organize the observed data in subsets corresponding to binary variables. In this paper, we address the problem of estimating one probability space given a set of observed data about $n$n variables or properties. One problem with estimating one single probability space is the exponential number of events. Approximation is one approach to addressing the problem of the exponential order of the number of events. Alternatively to approximation, we change paradigm – from classical, set-based probability spaces based on sets to quantum probability spaces based on vector subspaces. By changing paradigm, we leverage quantum probability and present an efficient algorithm to calculate a Quantum Probability Space (QPS) in only $O(n^4)$O(n4) steps. [ABSTRACT FROM AUTHOR]

The rivality index (RI) is a normalized distance measurement between a molecule and their first nearest neighbours providing a robust prediction of the activity of a molecule based on the known activity of their nearest neighbours. Negative values of the RI describe molecules that would be correctly classified by a statistic algorithm and, vice versa, positive values of this index describe those molecules detected as outliers by the classification algorithms. In this paper, we have described a classification algorithm based on the RI and we have proposed four weighted schemes (kernels) for its calculation based on the measuring of different characteristics of the neighbourhood of molecules for each molecule of the dataset at established values of the threshold of neighbours. The results obtained have demonstrated that the proposed classification algorithm, based on the RI, generates more reliable and robust classification models than many of the more used and well-known machine learning algorithms. These results have been validated and corroborated by using 20 balanced and unbalanced benchmark datasets of different sizes and modelability. The classification models generated provide valuable information about the molecules of the dataset, the applicability domain of the models and the reliability of the predictions. [ABSTRACT FROM AUTHOR]

Drawing from memory the face of a friend you have not seen in years is a difficult task. However, if you happen to cross paths, you would easily recognize each other. The biological memory is equipped with an impressive compression algorithm that can store the essential, and then infer the details to match perception. The Willshaw Memory is a simple abstract model for cortical computations which implements mechanisms of biological memories. Using our recently proposed sparse coding prescription for visual patterns [34] , this model can store and retrieve an impressive amount of real-world data in a fault-tolerant manner. In this paper, we extend the capabilities of the basic Associative Memory Model by using a Multiple-Modality framework. In this setting, the memory stores several modalities (e.g., visual, or textual) of each pattern simultaneously. After training, the memory can be used to infer missing modalities when just a subset is perceived. Using a simple encoder-memory-decoder architecture, and a newly proposed iterative retrieval algorithm for the Willshaw Model, we perform experiments on the MNIST dataset. By storing both the images and labels as modalities, a single Memory can be used not only to retrieve and complete patterns but also to classify and generate new ones. We further discuss how this model could be used for other learning tasks, thus serving as a biologically-inspired framework for learning. [ABSTRACT FROM AUTHOR]

To find the best-performing algorithms to distinguish type 1 and type 2 diabetes in administrative data. Embase and MEDLINE databases were searched from January 2000 until January 2023. Papers evaluating the performance of algorithms to define type 1 and type 2 diabetes by reporting diagnostic metrics against a range of reference standards were selected. Study quality was evaluated using the Quality Assessment of Diagnostic Accuracy Studies. Of the 24 studies meeting the eligibility criteria, 19 demonstrated a low risk of bias and low concerns about the applicability of the study population across all domains. Algorithms considering multiple diabetes diagnostic codes alone were sensitive and specific approaches to classify diabetes type (both metrics >92.1% for type 1 diabetes; >86.9% for type 2 diabetes). Among the top 10-performing algorithms to detect type 1 and type 2 diabetes, 70% and 100% featured multiple criteria, respectively. Information on insulin use was more sensitive and specific for detecting diabetes type than were criteria based on use of oral hypoglycaemic agents. Algorithms based on multiple diabetes diagnostic codes and insulin use are the most accurate approaches to distinguish type 1 from type 2 diabetes using administrative data. Approaches with more than one criterion may also increase sensitivity in distinguishing diabetes type. [ABSTRACT FROM AUTHOR]

Ensemble pruning is an important task in supervised learning because of the performance and efficiency advantage it begets to predictive modelling. Performance based empirical comparison (primarily on accuracy) is the most common modus operandi for critical evaluation of ensembles pruned by different algorithms. Deep analysis of existing literature reveals that ensemble size is an ignored attribute while judging the quality of ensembles. In this paper, we argue that the cost-effectiveness of an ensemble is a function of both performance and size. Hence, equitable comparison of two ensembles must take into account both these attributes to judge their relative merits. Following this argument, we propose an objective function called accrual function which quantifies the difference in performance and size of two ensembles, to gauge their relative cost-effectiveness. The function can be parameterized and has nice mathematical properties. Semantic interpretations of these properties are delineated in the paper. Finally, we apply the accrual function on published results from selected publications and demonstrate its ability to beget clarity while comparing ensembles. [ABSTRACT FROM AUTHOR]

Deep learning breakthrough stimulates new research trends in civil infrastructure inspection, whereas the lack of quality-guaranteed, human-annotated, free-of-charge, and publicly available defect datasets with sufficient amounts of data hinders the progress of deep learning in defect inspection. To boost research in deep learning-based visual defect inspection, this paper first reviews and summarizes 40 publicly available defect datasets, covering common defects in various types of buildings and infrastructures. The taxonomy of the datasets is proposed based on specific deep learning objectives (classification, segmentation, and detection). Clarifications are also made for each dataset regarding its corresponding data volume, data resolution, data source, defect categories covered, infrastructure types focused, material types targeted, algorithms adopted for validation, annotation levels, context levels, and publication license for future utilization. Consequently, the summarized defect datasets offer around 13. 38 M labeled images, cover more than 5 defect types, 5 infrastructure types, 5 material types, and 3 levels of image context. Given that the crack is a common interest in civil engineering, this paper further combines existing datasets with self-labeled crack images to establish a benchmark dataset providing more than 15,000 and 11,000 labeled images for crack classification and segmentation, respectively. Based on the established crack dataset, experiments are conducted for classification, segmentation, and the subsequent non-maximum suppression-based detection tasks. The proposed multi-branch self-attention module and multi-stage-fused attentional pyramid network have been successfully adapted into the state-of-the-art (SOTA) classification network- Swin Transformer and segmentation networks including DeepLab V3+ , DenseNet , and Full Resolution ResNet. The resulting classification network achieves 88.0% accuracy, and the adapted segmentation models reach 77. 8 % , 77. 6 % , 76. 9 % mIoU (mean Intersection over Union), respectively. Moreover, a comprehensive comparison between 11 SOTA classification algorithms and 12 SOTA segmentation algorithms has been conducted. The algorithms proposed in this work are shown to achieve satisfactory performance with an acceptable efficiency on modern graphic processing units. Detailed suggestions are provided for constructing high-quality datasets and inspection algorithms. Finally, this paper remarks on the quantity, diversity, difficulty, and scalability of the reviewed defect datasets, feasibility on robotic platforms, superiority of proposed algorithms, and criticality of algorithm comparison results, formulating a solid baseline for future defect inspection research. • Review of the datasets for deep learning-based visual defect inspection. • Comparison of the algorithms for defect classification, segmentation and detection. • Proposed deep learning-based network architectures for defect inspection. • Suggestions on developing defect datasets and defect inspection algorithms. [ABSTRACT FROM AUTHOR]

We investigate the use of Twitter data to deliver signals for syndromic surveillance in order to assess its ability to augment existing syndromic surveillance efforts and give a better understanding of symptomatic people who do not seek healthcare advice directly. We focus on a specific syndrome—asthma/difficulty breathing. We outline data collection using the Twitter streaming API as well as analysis and pre-processing of the collected data. Even with keyword-based data collection, many of the tweets collected are not be relevant because they represent chatter, or talk of awareness instead of an individual suffering a particular condition. In light of this, we set out to identify relevant tweets to collect a strong and reliable signal. For this, we investigate text classification techniques, and in particular we focus on semi-supervised classification techniques since they enable us to use more of the Twitter data collected while only doing very minimal labelling. In this paper, we propose a semi-supervised approach to symptomatic tweet classification and relevance filtering. We also propose alternative techniques to popular deep learning approaches. Additionally, we highlight the use of emojis and other special features capturing the tweet’s tone to improve the classification performance. Our results show that negative emojis and those that denote laughter provide the best classification performance in conjunction with a simple word-level n-gram approach. We obtain good performance in classifying symptomatic tweets with both supervised and semi-supervised algorithms and found that the proposed semi-supervised algorithms preserve more of the relevant tweets and may be advantageous in the context of a weak signal. Finally, we found some correlation (r = 0.414, p = 0.0004) between the Twitter signal generated with the semi-supervised system and data from consultations for related health conditions. [ABSTRACT FROM AUTHOR]

In this paper, Lie algorithm is used to classify the classical symmetry of a general diffusion-convection equation. The solution process is elucidated for different conditions, and the obtained symmetries can be used to study the solution properties of the diffusion-convection equation. [ABSTRACT FROM AUTHOR]

Due to lack of a central bureaucrat in mobile ad hoc networks, the security of the network becomes serious issue. During malicious attacks, according to the motivation of intruder the severity of the threat varies. It may lead to loss of data, energy or throughput. This paper proposes a lightweight Intruder Node Detection and Isolation Action mechanism (INDIA) using feature extraction, feature optimization and classification techniques. The indirect and direct trust features are extracted from each node and the total trust feature is computed by combining them. The trust features are extracted from each node of MANET and these features are optimized using Particle Swarm Optimization (PSO) algorithm as feature optimization technique. These optimized feature sets are then classified using Neural Networks (NN) classifier which identifies the intruder node. The performance of the proposed methodology is studied in terms of various parameters such as success rate in packet delivery, delay in communication and the amount of energy consumption for identifying and isolating the intruder. [ABSTRACT FROM AUTHOR]

Due to the variability of the radiated signal of the sonar targets, passive sonar target classification is a challenging problem in the real world application. Adaptive Mel-frequency cepstral coefficients (MFCCs) and multi-layer perceptron (MLP) approaches are proposed, including cepstral features to alleviate dataset's dimension and MLP network to adapt the variability in changing condition. In spite of the capabilities of MLP networks, low classification accuracy, and getting stuck in local minima are the main shortcomings of MLP networks. To overcome these shortcomings, this paper proposes the use of the newly introduced salp swarm algorithm (SSA) for training MLP network. In order to investigate the efficiency of the proposed classifier, four high-dimensional benchmark functions, as well as an experimental passive sonar data set, are employed. The designed classifier is compared to gray wolf optimizer (GWO), biogeography-based optimization (BBO), interior search algorithm (ISA), and group method of data handling (GMDH) in terms of classification accuracy, entrapment in local minima, and convergence speed. The results showed that the proposed classifier is more efficient than the other benchmark algorithms; therefore, the SSA classifies sonar data set as much as 0.9017 percent better than GMDH being the best results among the other classifiers. [ABSTRACT FROM AUTHOR]

Learning a classifier from imbalanced data is a challenging problem in Machine learning. A dataset is said to be imbalanced when the number of instances belonging to one class is much less than the number of instances belonging to the other class. Classifiers that proves efficient on standard data fail when the data is imbalanced as they are over trained by the majority class instances. Since class imbalance is a common characteristic of real-world data, the need for better classifiers becomes essential. This paper proposes a novel instance-based classification algorithm called Weighted Pattern Matching based Classification (PMC+) for classifying imbalanced data. PMC+ classifies unlabelled instances by computing the absolute difference between the feature values of the instances in the dataset and the unlabelled instance. PMC+ employs a simple classification procedure with weights and shows reasonably good performance. To improve the performance of PMC+, Fireworks based Feature and Weight Selection algorithm based on the idea of PMC+ has been proposed. PMC+ is evaluated on 44 binary imbalanced datasets and 15 multiclass imbalanced datasets. Although PMC+ does not employ a resampling or cost-sensitive method, experiments show that PMC+ is effective for classification of imbalanced data. The results of the experiments were validated using various non-parametric statistical tests. [ABSTRACT FROM AUTHOR]

Credit scoring focuses on the development of empirical models to support the financial decision‐making processes of financial institutions and credit industries. It makes use of applicants' historical data and statistical or machine learning techniques to assess the risk associated with an applicant. However, the historical data may consist of redundant and noisy features that affect the performance of credit scoring models. The main focus of this paper is to develop a hybrid model, combining feature selection and a multilayer ensemble classifier framework, to improve the predictive performance of credit scoring. The proposed hybrid credit scoring model is modeled in three phases. The initial phase constitutes preprocessing and assigns ranks and weights to classifiers. In the next phase, the ensemble feature selection approach is applied to the preprocessed dataset. Finally, in the last phase, the dataset with the selected features is used in a multilayer ensemble classifier framework. In addition, a classifier placement algorithm based on the Choquet integral value is designed, as the classifier placement affects the predictive performance of the ensemble framework. The proposed hybrid credit scoring model is validated on real‐world credit scoring datasets, namely, Australian, Japanese, German‐categorical, and German‐numerical datasets. [ABSTRACT FROM AUTHOR]

The modelability index of a dataset of molecules is a measurement of the capacity of the dataset to be modeled using a QSAR algorithm. This measure allows to predict the correct classification rate of the dataset counting the nearest neighbors to the molecules of the dataset belonging to their same class. In this paper, we propose a new measure for the prediction of the modelability of datasets based on the use of the nearest neighbors based rivality index and the centroids based rivality index. These indexes take into account the noise that the nearest neighbor belonging to a different class could generate in the results of the QSAR classification algorithm. Using thirty benchmark datasets, two types of dataset representation and six different algorithms, we show the excellent behavior of the proposed indexes, obtaining correlations with values of R2 greater than 0.9 between the correct classification rate obtained in the classification processes using five folds cross-validation and the modelability index calculated using the centroid based rivality index. [ABSTRACT FROM AUTHOR]

One effective technique that has recently been considered for solving classification problems is parametric ν -support vector regression. This method obtains a concurrent learning framework for both margin determination and function approximation and leads to a convex quadratic programming problem. In this paper we introduce a new idea that converts this problem into an unconstrained convex problem. Moreover, we propose an extension of Newton's method for solving the unconstrained convex problem. We compare the accuracy and efficiency of our method with support vector machines and parametric ν -support vector regression methods. Experimental results on several UCI benchmark data sets indicate the high efficiency and accuracy of this method. [ABSTRACT FROM AUTHOR]

Supervised classification is one of the most powerful techniques to analyze data, when a-priori information is available on the membership of data samples to classes. Since the labeling process can be both expensive and time-consuming, it is interesting to investigate semi-supervised algorithms that can produce classification models taking advantage of unlabeled samples. In this paper we propose LapReGEC, a novel technique that introduces a Laplacian regularization term in a generalized eigenvalue classifier. As a result, we produce models that are both accurate and parsimonious in terms of needed labeled data. We empirically prove that the obtained classifier well compares with other techniques, using as little as 5% of labeled points to compute the models. [ABSTRACT FROM AUTHOR]

Many malware families such as botnet utilize domain generation algorithms (DGAs) to evade detection at present. The mainstream detection algorithms based on artificial rules and machine learning have some limitations due to the fact that DGAs generate domain characters timely and rapidly. The former is somewhat blind to new DGA variants. The latter suffers from the lack of evolving training data. In order to solve these problems, this paper defined domain encoder and decoder on account of the method of ASCII encoding and combined them with the concept of generative adversarial network (GAN) to construct domain character generator. Then it used the generator to predict and generate DGA variants. Experiment results show that the DGA variants generated by this method can act as real DGA samples when these variants are utilized to train and estimate classifiers. This verifies the validity of the generated data and they can be effectively utilized to train and estimate DGA domain detector. [ABSTRACT FROM AUTHOR]

High dimensional biomedical data contain tens of thousands of features, accurate and effective identification of the core features in these data can be used to assist diagnose related diseases. However, there are often a large number of irrelevant or redundant features in biomedical data, which seriously affect subsequent classification accuracy and machine learning efficiency. To solve this problem, a novel filter feature selection algorithm based on redundant removal (FSBRR) is proposed to classify high dimensional biomedical data in this paper. First of all, two redundant criteria are determined by vertical relevance (the relationship between feature and class attribute) and horizontal relevance (the relationship between feature and feature). Secondly, to quantify redundant criteria, an approximate redundancy feature framework based on mutual information (MI) is defined to remove redundant and irrelevant features. To evaluate the effectiveness of our proposed algorithm, controlled trials based on typical feature selection algorithm are conducted using three different classifiers, and the experimental results indicate that the FSBRR algorithm can effectively reduce the feature dimension and improve the classification accuracy. In addition, an experiment of small sample dataset is designed and conducted in the section of discussion and analysis to clarify the specific implementation process of FSBRR algorithm more clearly. [ABSTRACT FROM AUTHOR]

Abstract In streaming data classification, most of the existing methods assume that all arrived evolving data are completely labeled. One challenge is that some applications where only small amount of labeled examples are available for training. Incremental semi-supervised learning algorithms have been proposed for regularizing neural networks by incorporating various side information, such as pairwise constraints or user-provided labels. However, it is hard to put them into practice, especially for non-stationary environments due to the effectiveness and parameter sensitivity of such algorithms. In this paper, we propose a novel incremental semi-supervised learning framework on streaming data. Each layer of model is comprised of a generative network, a discriminant structure and the bridge. The generative network uses dynamic feature learning based on autoencoders to learn generative features from streaming data which has been demonstrated its potential in learning latent feature representations. In addition, the discriminant structure regularizes the network construction via building pairwise similarity and dissimilarity constraints. It is also used for facilitating the parameter learning of the generative network. The network and structure are integrated into a joint learning framework and bridged by enforcing the correlation of their parameters, which balances the flexible incorporation of supervision information and numerical tractability for non-stationary environments as well as explores the intrinsic data structure. Moreover, an efficient algorithm is designed to solve the proposed optimization problem and we also give an ensemble method. Particularly, when multiple layers of model are stacked, the performance is significantly boosted. Finally, to validate the effectiveness of the proposed method, extensive experiments are conducted on synthetic and real-life datasets. The experimental results demonstrate that the performance of the proposed algorithms is superior to some state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Due to its simplicity, efficiency and efficacy, naive Bayes (NB) continues to be one of the top 10 data mining algorithms. A mass of improved approaches to NB have been proposed to weaken its conditional independence assumption. However, there has been little work, up to the present, on instance weighting filter approaches to NB. In this paper, we propose a simple, efficient, and effective instance weighting filter approach to NB. We call it attribute (feature) value frequency-based instance weighting and denote the resulting improved model as attribute value frequency weighted naive Bayes (AVFWNB). In AVFWNB, the weight of each training instance is defined as the inner product of its attribute value frequency vector and the attribute value number vector. The experimental results on 36 widely used classification problems show that AVFWNB significantly outperforms NB, yet at the same time maintains the computational simplicity that characterizes NB. [ABSTRACT FROM AUTHOR]

Aiming at the reasonable placement of different business contents in content centric networking (CCN), this paper proposed a hybrid caching scheme based on service classification and node partition (SCNP). For streaming media-on-de-mand services, this scheme used push-pull cache strategy based on popularity to achieve its sequential storage in the edge network. For non-streaming contents, this scheme used hash-based explicit cache strategy to implement its placement of single copy in the core network. Simulation results show that compared with the classic cache algorithms, this scheme can improve the cache hit ratio and the hop reduction ratio, and reduce the average request delay. [ABSTRACT FROM AUTHOR]

Aiming at the low precision problem while the cost was introduced into attribute reduction of decision-theoretic rough set, this paper studied the balance between the total cost and the precision in classification. It used the total cost of the classification and the approximate classification quality as the constrained criteria in the attribute reduction procedure, combined with simulated annealing method, and proposed a DTRS attribute reduction algorithm constrained by cost-sensitive and classification quality (hereinafter referred as ARACOQ). It carried out the simulation experiments by using UC1 data set. The results verify the effectiveness of the ARACOQ algorithm. which can find an attribute reduction set with the highest classification precision within the affordable cost range. [ABSTRACT FROM AUTHOR]

Highlights • A hybrid semantic enrichment framework to classify event-related tweets • Combines semantic enrichment, external documents and named-entity recognition • A semantic feature pruning algorithm and feature selection to deal with huge volume of features • Assessment of each framework component using a broad experimental setup • Superior results compared to a textual baseline and a word embeddings baseline Abstract Twitter has become instrumental as a means of spreading information, opinions or awareness about real-world events. The classification of event-related tweets is a challenging problem since tweets are noisy and sparse pieces of text that lack contextual information. Related work proposes contextual enrichment techniques using external sources (e.g. semantic web, external documents), often considering underlying assumptions about the target events. However, they lack guidelines for determining the textual features to enrich, the external sources to use, the properties to explore, and how to prevent the inclusion of unrelated information. In this paper, we propose a hybrid semantic enrichment framework for the classification of event-related tweets. We contribute to this field by leveraging different contextual enrichment strategies into a unifying framework targeted at a broad range of event types, where each enrichment technique has a role in the improvement of event classification. The framework also encompasses a solution to deal with the huge number of features that result from semantic enrichment, which combines a pruning method to select domain relevant semantic features and general-purpose feature selection techniques. We assessed the contribution of each framework component to event classification improvement using a broad experimental setting. Using seven events of distinct natures, we outperformed a word embeddings baseline in 93.6% of cases, and a textual baseline in 60.3% of cases. In most cases, we improved the recall, with no significant impact on the precision. [ABSTRACT FROM AUTHOR]

This paper highlights the importance of optimized shape index for agricultural management system analysis that utilizes the contiguous bands of hyperspectral data to define the gradient of the spectral curve and improve image classification accuracy. Currently, a number of machine learning methods would resort to using averaged spectral information over wide bandwidths resulting in loss of crucial information available in those contiguous bands. The loss of information could mean a drop in the discriminative power when it comes to land cover classes with comparable spectral responses, as in the case of cultivated fields versus fallow lands. In this study, we proposed and tested three new optimized novel algorithms based on Moment Distance Index (MDI) that characterizes the whole shape of the spectral curve. The image classification tests conducted on two publicly available hyperspectral data sets (AVIRIS 1992 Indian Pine and HYDICE Washington DC Mall images) showed the robustness of the optimized algorithms in terms of classification accuracy. We achieved an overall accuracy of 98% and 99% for AVIRIS and HYDICE, respectively. The optimized indices were also time efficient as it avoided the process of band dimension reduction, such as those implemented by several well-known classifiers. Our results showed the potential of optimized shape indices, specifically the Moment Distance Ratio Right/Left (MDRRL), to discriminate between types of tillage (corn-min and corn-notill) and between grass/pasture and grass/trees, tree and grass under object-based random forest approach. [ABSTRACT FROM AUTHOR]

Organizations should consider process improvement for promoting their performance. The main problem is the high volume of process dataset along with the wide variety of their features that leads to increase the complexity in process improvement methods. Previous methods cannot improve processes in a high volume of process dataset. Data mining approach can support improvement methods by identifying valuable hidden patterns in high volume of process dataset. In this paper a framework of using data mining techniques for extracting valuable hidden patterns in high volume of process dataset is developed for presenting the improvement suggestions. In order to evaluate the proposed framework, a real set of processes along with their features were gathered. After that, using classification, clustering, and features selection algorithms, valuable patterns in high volume of process dataset were identified. After evaluating these patterns, improvement suggestions were recommended by the identified patterns. The results showed that identified patterns can support processes improvement activities with recommending the improvement suggestions. [ABSTRACT FROM AUTHOR]

Aiming at optimizing and improving a hybrid collaborative filtering based on Spark platform for its sparsity, scalability and personalized recommendation by using the method of algorithm integration, this paper took the model of Stacking integration to integrate multiple weak recommender units in a linearly weighted into a comprehensive recommender. Firstly, this algorithm optimized the collaborative filtering based on the nearest neighbor by presorting and adjusting the similarity calculation strategy with popularity and praise degree, and improved the rationality and complexity of similarity calculation. It solved the problem of score sparsity to some extent. At the same time, this algorithm integrated closely distributed computing platform, which could make full use of the advantages of distributed platform to design and implement an increment iterative model of recommendation algorithm by using the Spark streaming and distributed storage structure. It solved the problem that collaborative filtering algorithm was hard to expand and made poor real-time performance. The experimental data used UCI public data set named MovieLens and NetFlix films’ score. The experimental results show that the improved algorithm has a good performance and makes great progress in personalized recommendation, accuracy and scalability compared with the previous algorithms. It provides a feasible algorithm integration scheme for the application of the recommended system. [ABSTRACT FROM AUTHOR]

Document representation is a key problem in document analysis and processing tasks, such as document classification, clustering and information retrieval. Especially for unstructured text data, the use of a suitable document representation method would affect the performance of the subsequent algorithms for applications and research. In this paper, we propose a novel document representation method called the conditional co-occurrence degree matrix document representation method (CCODM), which is based on word co-occurrence. CCODM not only considers the co-occurrence of terms but also considers the conditional dependencies of terms in a specific context, which leads to more available and useful structural and semantic information being retained from the original documents. Extensive experimental classification results with different supervised and unsupervised feature selection methods show that the proposed method, CCODM, achieves better performance than the vector space model, latent Dirichlet allocation, the general co-occurrence matrix representation method and the document embedding method. [ABSTRACT FROM AUTHOR]

Multi-label learning has been widely used in many application scenarios right now. In this kind of learning problem, each instance is simultaneously assigned with more than one class label. Since different class labels might had their own unique characteristics(such as label-specific feature) which would be more useful for label classification, so some multi-label learning approaches based on label-specific features had already been proposed. Therefore, aiming at the problem that redundant feature space caused by label-specific feature construction, this paper proposed a multi-label label-specific feature extraction algorithm named LIFT_RSM, which could improve the performance of classification by comprehensively using random subspace method and the thought of pair-wise constraint dimensionality reduction to extract effective feature information in labelspecific feature space. The experimental results on several datasets show that the proposed algorithm can achieve better classification results compared with several classical multi-label algorithms. [ABSTRACT FROM AUTHOR]