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In this paper, eight variants of the Whale Optimisation Algorithm (WOA), that are based on eight different transfer functions, are introduced and used as search strategies in a wrapper feature selection model. Feature selection is a challenging task in machine learning process. It aims to minimise the size of a dataset by removing redundant and/or irrelevant features, with no information loss, to improve the efficiency of the learning algorithms. The used transfer functions belong to two different families; S-shaped and V-shaped. The proposed approaches have been tested on nine different high-dimensional medical datasets, with a low number of samples and multiple classes. The results revealed a superior performance for the V-shaped based approaches over the the S-shaped approaches. Moreover, the results of the V-shaped approach is compared with well-known feature selection approaches, and the superiority of the proposed approach is proven. [ABSTRACT FROM AUTHOR]

In the bioinformatics field, the classification of unknown biological sequences is a key task that is fundamental for simplifying the consistency, aggregation, and survey of organisms and their evolution. We can view biological sequences as data components of higher non-fixed dimensions, corresponding to the length of the sequences. Numerical encoding performs an important function in DNA sequence evaluation via computational procedures such as one-hot encoding (OHE). However, the OHE method has drawbacks: 1) it does not add any details that may produce the additional predictive variable, and 2) if the variable has many classes, then OHE increases the feature space significantly. To overcome these drawbacks, this paper presents a computationally effective framework for classifying DNA sequences of living organisms in the image domain. The proposed strategy relies upon multilayer perceptron trained by a pseudoinverse learning autoencoder (PILAE) algorithm. The PILAE training process does not have to set the learning control parameters or indicate the number of hidden layers. Therefore, the PILAE classifier can accomplish better performance contrasting with other deep neural network (DNNs) strategies such as VGG-16 and Xception models. Experimental results have demonstrated that this proposed strategy achieves high prediction accuracy as well as to a significant degree high computational efficiency over different datasets. [ABSTRACT FROM AUTHOR]

Deep neural networks are vulnerable to the attack of adversarial examples.To solve this problem,some works trained networks by adding Gaussian noise to the image,thereby improving the ability of the network to defend adversarial examples.But the method did not consider the sensitivity of the network to different areas in the image when adding noise.To solve this problem,this paper proposed an adversarial training algorithm based on gradient guidance noise addition.When training the network,it added adaptive noise to different areas based on the sensitivity,added large noise to the more sensitive areas,suppressed the sensitivity of the network to image changes,added less noise to the less sensitive areas and improved the network classification accuracy.Compared with the existing algorithms on the Cifar-10 dataset,the experimental results show that the proposed method effectively improves the accuracy of neural networks when classifying adversarial examples. [ABSTRACT FROM AUTHOR]

To filter out the false positive patterns returned from the discriminative sequential patterns mining methods,this paper proposed a standard permutation method called SP-DSP.This method firstly mined frequent sequential patterns by the GSP algorithm,then eliminated the patterns whose growth rate were less than the threshold.Finally,it used the standard permutation method to compute the p-values of tested patterns.As a result,the number of false positive patterns could be controlled under the multiple hypothesis testing measures.The experiments show that the SP-DSP algorithm can alleviate a lot of false positive patterns and retain as many true patterns as possible,which improves the accuracy of the downstream classification tasks. [ABSTRACT FROM AUTHOR]

Distance metric learning is a branch of machine learning that aims to learn distances from the data, which enhances the performance of similarity-based algorithms. This tutorial provides a theoretical background and foundations on this topic and a comprehensive experimental analysis of the most-known algorithms. We start by describing the distance metric learning problem and its main mathematical foundations, divided into three main blocks: convex analysis, matrix analysis and information theory. Then, we will describe a representative set of the most popular distance metric learning methods used in classification. All the algorithms studied in this paper will be evaluated with exhaustive testing in order to analyze their capabilities in standard classification problems, particularly considering dimensionality reduction and kernelization. The results, verified by Bayesian statistical tests, highlight a set of outstanding algorithms. Finally, we will discuss several potential future prospects and challenges in this field. This tutorial will serve as a starting point in the domain of distance metric learning from both a theoretical and practical perspective. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid method has been introduced to improve the classification performance of naïve Bayes (NB) for the mixed dataset and multi-class problems. This proposed method relies on a similarity measure which is applied to portions that are not correctly classified by NB. Since the data contains a multi-valued short text with rare words that limit the NB performance, we have employed an adapted selective classifier based on similarities (CSBS) classifier to exceed the NB limitations and included the rare words in the computation. This action has been achieved by transforming the formula from the product of the probabilities of the categorical variable to its sum weighted by numerical variable. The proposed algorithm has been experimented on card payment transaction data that contains the label of transactions: the multi-valued short text and the transaction amount. Based on K-fold cross validation, the evaluation results confirm that the proposed method achieved better results in terms of precision, recall, and F-score compared to NB and CSBS classifiers separately. Besides, the fact of converting a product form to a sum gives more chance to rare words to optimize the text classification, which is another advantage of the proposed method. [ABSTRACT FROM AUTHOR]

Aiming at the problems of numerous complaint factors and relatively poor fault samples of Internet protocol TV users, this paper proposed the LFOA-HSRVM algorithm as a prediction method for IPTV user's complaint combined with the outstanding sparsity of relevance vector machine . The method regarded fault prediction of IPTV user as a binary-classification problem with imbalanced data sets. The algorithm was based on the nuclear parameter optimization of RVM and hybrid sampling technology to overcome the problem that the decision boundary of the traditional RVM algorithm was biased to the minority class wh en dealing with imbalanced data sets. Compared with other algorithms, the experimental results show that the algorithm ' s performance of few classification and overall classification are improved greatly, and the effect of prediction is better. [ABSTRACT FROM AUTHOR]

An important issue in classifying rational 3-tangles is how to know whether or not a given tangle is the trivial rational 3-tangle called ∞-tangle. The author [4] provided a certain algorithm to detect the ∞-tangle. In this paper, we give a much simpler method to detect the ∞-tangle by using the bridge arc replacement. We hope that this method can help prove many application problems such as a classification of 3-bridge knots. • Bridge arc replacement. • Detecting ∞-tangle. [ABSTRACT FROM AUTHOR]

[Display omitted] The unstable statuses in machining, such as chatter and severe forced vibration, limit manufacturing productivity and quality. Although there have been plentiful studies on the on-line sensing signals and off-line surface images to identify these statuses qualitatively, the quantification of chatter severity, which is of great significance on machining stability, has been rarely reported. Workpiece surface morphology is the direct result and thus the objective criteria of unstable machining. This paper proposes a new algorithm to quantify the chatter status based on the milled surface image analysis of end-milling. Besides, the proposed method can also identify the stable, forced vibration, and chatter statuses. Specifically, this proposed algorithm extracts two indexes, called Index_Dmean and Index_Emean , via processing the milled surface image. The definitions and calculations of these two indexes are given in Section 2. Index_Dmean is used to differentiate stable and unstable surfaces and quantify the severity of chatter status, and Index_Emean can distinguish between forced vibration and chatter surfaces. Experimental results demonstrate the availability of the proposed algorithm on the chatter quantification and milling instability classification performance. In addition, an applicable on-line vision system with satisfying performance based on the algorithm is developed. [ABSTRACT FROM AUTHOR]

A key issue in RGBD classification is how to fuse the RGB and depth modalities. A popular way is to extract the private features of unimodality and the common features between the two modalities. Most of them use low order algebraic metrics to find the common part of the RGB and depth signals. In this paper, adversarial network is used to learn the common features between the RGB and depth modalities. A modality discriminator is designed to compete with the feature encoder so as to generate the modality-invariant information, which can be regarded as the common features. With this concept, we present a M ulti- M odal F eature L earning algorithm with A dversarial N etwork (MMFLAN) to decouple the RGBD signals and obtain the fused features. Comprehensive experiments based on three datasets are used to evaluate the effectiveness and robustness of our MMFLAN. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is the process of finding the least possible number of features that are able to describe a dataset in the same way as the original features. Feature selection is a crucial preprocessing step for data mining techniques as it improves the performance of the prediction process in terms of speed and accuracy and also provides a better understanding of stored data. The success of the FS process depends on achieving a balance between two important factors, namely selecting the minimal number of features and maintaining the maximum accuracy in the results. In this paper, two methods are proposed to improve the FS process. Firstly, the mine blast algorithm (MBA) is introduced to optimize the FS process in the exploration phase. Secondly, the MBA is hybridized with simulated annealing as a local search in the exploitation phase to enhance the solutions located by the MBA. The proposed approaches (MBA and MBA–SA) are tested on 18 benchmark datasets from the UCI repository, and the comprehensive experimental results indicate that MBA–SA achieved good performance when compared with five approaches in the literature. [ABSTRACT FROM AUTHOR]

Due to the high-speed requirement of high-end network devices, hardware using TCAMs has been the dominant implementation of packet classification in industry. Despite its capability for line-speed queries, TCAM is not only power hungry but also capacity inefficient. By making use of a pre-classifier to activate TCAM blocks selectively, many research efforts significantly reduce the power consumption of TCAM. However, these bottom-up based pre-classifiers achieve power savings at the expense of poor utilization of TCAM capacity, and the potential of power reduction is not fully exploited in many cases. This paper proposed a power-saving pre-classifier for TCAM based packet classification, which constructed based on decision trees. By grouping rules with respect to their small fields, rules could be recursively mapped into decision trees without the trouble of rule replications, so that a top-down traversal algorithm could be well applied for obtaining index items. Experimental results show that for rule sets up to one hundred thousand entries, the proposed design achieves 98. 2% power reduction with a TCAM storage overhead of 1. 3% on average. [ABSTRACT FROM AUTHOR]

Text classification is a popular method in data mining. It is utilized to get valuable information from the vast quantity of data. Feature selection is a crucial step in Text classification. It is a vital preprocessing technique for powerful data analysis, where only a subset from the original data features is chosen by removing noisy, irrelevant, or redundant features. In this paper, a feature selection method utilizing the combination of chi-square and Artificial Bee Colony (ABC) is proposed. Chi-square, a filter method that is computationally fast, simple and has the ability to deal with a large dimensional feature, is used as the first level of the feature selection process. After that, the wrapper method, Artificial Bee Colony algorithm, is used as the second level where Naive Base is used as a fitness function. The results showed that a reduced number of features outperformed classification accuracy to that using the original features set. Furthermore, the proposed method had a better performance compared with the chi-square method and the ABC algorithm as a feature selection method. [ABSTRACT FROM AUTHOR]

The problem of attaining energy efficiency in distributed systems is of importance, but a general, non-domain-specific theory of energy-minimal scheduling is far from developed. In this paper, we classify the problems of energy-minimal scheduling and present theoretical foundations of the same. We derive results concerning energy-minimal scheduling of independent jobs in a distributed system with functionally similar machines with different working and idle power ratings. The machines considered in our system can have identical as well as different speeds. If the jobs can be divided into arbitrary parts, we show that the minimum-energy schedule can be generated in linear time and give exact scheduling algorithms. For the cases where jobs are non-divisible, we prove that the scheduling problems are NP-hard and also give approximation algorithms for the same along with their bounds. [ABSTRACT FROM AUTHOR]

In this paper, we propose Recycling+, an algorithm that uses temporal information for video frame classification with noisy label. With the proposed algorithm, the network can be trained robustly through not only clean samples with original labels but also noisy samples with corrected labels obtained by temporal average predictions. [ABSTRACT FROM AUTHOR]

We consider the class G(Sn) of orientation preserving Morse–Smale diffeomorphisms of the sphere Sn of dimension n > 3, assuming that invariant manifolds of different saddle periodic points have no intersection. For any diffeomorphism f ∈ G(Sn), we define a coloured graph Γf that describes a mutual arrangement of invariant manifolds of saddle periodic points of the diffeomorphism f. We enrich the graph Γf by an automorphism Pf induced by dynamics of f and define the isomorphism notion between two coloured graphs. The aim of the paper is to show that two diffeomorphisms f, f′ ∈ G(Sn) are topologically conjugated if and only if the graphs Γf, Γf′ are isomorphic. Moreover, we establish the existence of a linear-time algorithm to distinguish coloured graphs of diffeomorphisms from the class G(Sn). [ABSTRACT FROM AUTHOR]

Aiming at the problem of the discriminator' s poor classification accuracy on a small number of labeled samples and insufficient robustness to the local perturbation of manifolds in the generative adversarial network, this paper proposed a generative adversarial network based on variable loss and manifold regularization. The algorithm used a variable loss instead of the original discriminator to solve the adverse effect of the poorly trained classifier on the semi-supervised classification task. In addition, on the basis of variable loss of discriminator, it added manifold regular terms to improve the robustness of discriminator to local disturbance by punishing the variation of classification decision of discriminator on manifold. Using the quality of the generated samples and the semi-supervised classification accuracy as the evaluation criteria of the algorithm, it performed numerical experiments on the dataset SVHN and CIFAR-10. Comparing with other semi-supervised algorithms, the results show that the proposed algorithm can obtain higher quality generated samples and higher precision classification results with a small amount of labeled data. [ABSTRACT FROM AUTHOR]

Atom Search Optimisation (ASO) is a recently proposed metaheuristic algorithm that has proved to work effectively on several benchmark tests. In this paper, we propose the binary variants of atom search optimisation (BASO) for wrapper feature selection. In the proposed scheme, eight transfer functions from S-shaped and V-shaped families are used to convert the continuous ASO into the binary version. The proposed BASO approaches are employed to select a subset of significant features for efficient classification. Twenty-two well-known benchmark datasets acquired from the UCI machine learning repository are used for performance validation. In the experiment, the BASO with an optimal transfer function that contributes to the best classification performance is presented. The particle swarm optimisation (PSO), binary differential evolution (BDE), binary bat algorithm (BBA), binary flower pollination algorithm (BFPA), and binary salp swarm algorithm (BSSA) are used to evaluate the efficacy and efficiency of proposed approaches in feature selection. Our experimental results reveal the superiority of proposed BASO not only in high prediction accuracy but also in the minimal number of selected features. [ABSTRACT FROM AUTHOR]

The qualities of new data used in the sequential learning phase of the online sequential extreme learning machine algorithm (OS-ELM) have a significant impact on the performance of OS-ELM. This paper proposes a novel data filter mechanism for OS-ELM from the perspective of fuzziness and a fuzziness-based online sequential extreme learning machine algorithm (FOS-ELM). In FOS-ELM, when new data arrive, a fuzzy classifier first picks out the meaningful data according to the fuzziness of each sample. Specifically, the new samples with high-output fuzziness are selected and then used in sequential learning. The experimental results on eight binary classification problems and three multiclass classification problems have shown that FOS-ELM updated by the new samples with high-output fuzziness has better generalization performance than OS-ELM. Since the unimportant data are discarded before sequential learning, FOS-ELM can save more memory and have higher computational efficiency. In addition, FOS-ELM can handle data one-by-one or chunk-by-chunk with fixed or varying sizes. The relationship between the fuzziness of new samples and the model performance is also studied in this paper, which is expected to provide some useful guidelines for improving the generalization ability of online sequential learning algorithms. [ABSTRACT FROM AUTHOR]

Multiple instance learning (MIL) is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag. This formulation is gaining interest because it naturally fits various problems and allows to leverage weakly labeled data. Consequently, it has been used in diverse application fields such as computer vision and document classification. However, learning from bags raises important challenges that are unique to MIL. This paper provides a comprehensive survey of the characteristics which define and differentiate the types of MIL problems. Until now, these problem characteristics have not been formally identified and described. As a result, the variations in performance of MIL algorithms from one data set to another are difficult to explain. In this paper, MIL problem characteristics are grouped into four broad categories: the composition of the bags, the types of data distribution, the ambiguity of instance labels, and the task to be performed. Methods specialized to address each category are reviewed. Then, the extent to which these characteristics manifest themselves in key MIL application areas are described. Finally, experiments are conducted to compare the performance of 16 state-of-the-art MIL methods on selected problem characteristics. This paper provides insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research. Code is available on-line at https://github.com/macarbonneau/MILSurvey . [ABSTRACT FROM AUTHOR]

Background: High-throughput sequencing data are widely collected and analyzed in the study of complex diseases in quest of improving human health. Well-studied algorithms mostly deal with single data source, and cannot fully utilize the potential of these multi-omics data sources. In order to provide a holistic understanding of human health and diseases, it is necessary to integrate multiple data sources. Several algorithms have been proposed so far, however, a comprehensive comparison of data integration algorithms for classification of binary traits is currently lacking. Results: In this paper, we focus on two common classes of integration algorithms, graph-based that depict relationships with subjects denoted by nodes and relationships denoted by edges, and kernel-based that can generate a classifier in feature space. Our paper provides a comprehensive comparison of their performance in terms of various measurements of classification accuracy and computation time. Seven different integration algorithms, including graph-based semisupervised learning, graph sharpening integration, composite association network, Bayesian network, semi-definite programming-support vector machine (SDP-SVM), relevance vector machine (RVM) and Ada-boost relevance vector machine are compared and evaluated with hypertension and two cancer data sets in our study. In general, kernel-based algorithms create more complex models and require longer computation time, but they tend to perform better than graph-based algorithms. The performance of graph-based algorithms has the advantage of being faster computationally. Conclusions: The empirical results demonstrate that composite association network, relevance vector machine, and Ada-boost RVM are the better performers. We provide recommendations on how to choose an appropriate algorithm for integrating data from multiple sources. [ABSTRACT FROM AUTHOR]

In the last decade a large number of supervised learning methods have been introduced in the field of the machine learning. Supervised learning became an area for a lot of research activity in machine learning. Many of the supervised learning techniques have found application in their processing and analyzing variety of data. One of the main characteristics is that the supervised learning has the ability of annotated training data. The so called labels are class labels in the classification process. There is a variety of algorithms that are used in the supervised learning methods. This paper summarizes the fundamental aspects of couple of supervised methods. The main goal and contribution of this review paper is to present the overview of machine learning and provide machine learning 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]

Sparse representation and dictionary learning have been successfully applied in hyperspectral image classification. Generally, it is more effective to learn the sub-dictionary for each class and utilize multiple scale strategy. However, the sub-dictionary may only consider the within-class information and ignore the discriminative information. For multiscale sparse representation, the shape of the regions would not adaptively change according to context structure. This paper proposes a discriminative sub-dictionary learning algorithm and an adaptive multiscale superpixel classification strategy under sparse representation framework for hyperspectral image classification. In dictionary learning stage, the global constraint term can ensure the learnt sub-dictionaries contain more discriminative information. In sparse representation stage, the adaptive multiscale superpixel strategy can exploit the spatial context according to the structural information. The combination of the two strategies can further explore the spatial and spectral information in hyperspectral image. Experiments on four hyperspectral image datasets prove that the proposed method has a significant improvement in both quantitative and qualitative analysis comparing with state-of-the-art algorithms. Moreover, the proposed algorithm can produce satisfactory results for the imbalanced data problem involved in hyperspectral image. [ABSTRACT FROM AUTHOR]

Linear discriminant analysis (LDA) is widely used for various binary classification problems. In contrast to the LDA that estimates the precision matrix Ω and the mean difference vector δ in the classification rule separately, the linear programming discriminant (LPD) rule estimates the product Ωδ directly through a constrained ℓ1 minimization. The LPD rule has very good classification performance on many high‐dimensional binary classification problems. However, to estimate β* = Ωδ, the LPD rule uses equal weights for all the elements of β* in the constrained ℓ1 minimization. It may not deliver the optimal estimate of β*, and therefore the estimated discriminant direction can be suboptimal. In order to obtain better estimates of β* and the discriminant direction, we can heavily penalize βj in the constrained ℓ1 minimization if we suspect the jth feature is useless for the classification while moderately penalize βj if we suspect the jth feature is useful. In this paper, based on the LPD rule and some popular feature screening methods, we propose a new weighted linear programming discriminant (WLPD) rule for the high‐dimensional binary classification problem. The screening statistics used in the marginal two‐sample t‐test screening, Kolmogorov–Smirnov filter, and the maximum marginal likelihood screening will be used to construct appropriate weights for different elements of β* flexibly. Besides the linear programming algorithm, we develop a new alternating direction method of multipliers algorithm to solve the high‐dimensional constrained ℓ1 minimization problem efficiently. Our numerical studies show that our proposed WLPD rule can outperform LPD and serve as an effective binary classification tool. [ABSTRACT FROM AUTHOR]

In this paper, an optimal weight semi-supervised learning machine for a single-hidden layer feedforward network (SLFN) with time delay is developed. Both input weights and output weights of the SLFN are globally optimized with manifold regularization. By feature mapping, input vectors can be placed at the prescribed positions in the feature space in the sense that the separability of all nonlinearly separable patterns can be maximized, unlabeled data can be leveraged to improve the classification accuracy when labeled data are scarce, and a high degree of recognition accuracy can be achieved with a small number of hidden nodes in the SLFN. Some simulation examples are presented to show the excellent performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

High dimensionality and sample imbalance of gene expression data promote the development of effective algorithms for classifying gene expression data. To improve the ability to distinguish different subtypes of gene expression data, we devise a hypervolume-based discrete evolutionary optimization algorithm (HYBDEOA) in this paper. Four objectives, namely the number of genes, the accuracy, the relevance, and the redundancy, are optimized simultaneously to guide the evolution. Firstly, binary encoding is used to choose some features, projecting data onto different subspaces. After that, a discrete neighborhood operation is conducted to generate a new binary-mapped population. Combining the new population with the current population, we employ the hypervolume-based mechanism to select the Pareto solutions. Finally, a discrete mutation method is proposed to find promising solutions in the binary search space. To demonstrate the performance of HYBDEOA, we apply HYBDEOA to 55 synthetic datasets and 35 cancer gene expression datasets. Extensive experiments are also conducted to reveal the effectiveness and efficiency of HYBDEOA. The experimental results demonstrate that our proposed method is a parameter-less and robust algorithm, which can group gene expression data with a finer and more informative classification. [ABSTRACT FROM AUTHOR]

In view of the existing machine learning methods in the field of high -resolution remote sensing image building extraction and the like, which requires positive and negative training samples to participate at the same time, a one- class building change detection algorithm based on one -class samples without the need for negative samples is proposed. Firstly, it extracts the morphological building index features of the image, and fuse multifeatures with the spectral features. Secondly, based on the one - class classification method proposed in this paper, from the object-based perspective, it gets the object-level building change detection results. Finally, it constructs a new shape feature which is refined to obtain the final building change detection result. Through experiments on multi-source high-resolution remote sensing images, it is verified that our proposed algorithm is robust and has better detection accuracy than existing building change detection algorithms. [ABSTRACT FROM AUTHOR]

The advancement of technology has resulted in a rapid increase in supernova (SN) discoveries. The Subaru/Hyper Suprime-Cam (HSC) transient survey, conducted from fall 2016 through spring 2017, yielded 1824 SN candidates. This gave rise to the need for fast type classification for spectroscopic follow-up and prompted us to develop a machine learning algorithm using a deep neural network with highway layers. This algorithm is trained by actual observed cadence and filter combinations such that we can directly input the observed data array without any interpretation. We tested our model with a dataset from the LSST classification challenge (Deep Drilling Field). Our classifier scores an area under the curve (AUC) of 0.996 for binary classification (SN Ia or non-SN Ia) and 95.3% accuracy for three-class classification (SN Ia, SN Ibc, or SN II). Application of our binary classification to HSC transient data yields an AUC score of 0.925. With two weeks of HSC data since the first detection, this classifier achieves 78.1% accuracy for binary classification, and the accuracy increases to 84.2% with the full dataset. This paper discusses the potential use of machine learning for SN type classification purposes. [ABSTRACT FROM AUTHOR]

The traditional measurement method of similarity ignores the project of multi-attribute category differences. To avoid this problem, this paper proposed a recommendation algorithm for improving project multi-attribute classification. Firstly, it used the project-user membership matrix to explore the affiliation and created a similar neighbor FP-Tree to extract the nearest neighbor set. Then it analyzed the common item similarity between users and the difference of the project multi-attribute classification, and used the weight factor to combine the common project with multi-attribute classification. It constructed the CNB model to measure the similarity degree of neighbors. Finally, it sorted the similar users in descending order to obtain more accurate similar users and complete the recommendation work. In virtue of the medical dataset, it verified the effectiveness of the proposed algorithm. The experimental results show that the recommendation accuracy of time complexity and mean of average accuracy have been improved. [ABSTRACT FROM AUTHOR]

• The greedy AutoAugment is proposed to find the best augmentation policies. • It reduces the exponential growth of the number of possible trials to linear growth. • The algorithm is computationally efficient and can concatenate many sub-policies. • The greedy approach helps the algorithm to increase the accuracy of the network. A major problem in data augmentation is to ensure that the generated new samples cover the search space. This is a challenging problem and requires exploration for data augmentation policies to ensure their effectiveness in covering the search space. In this paper, we propose Greedy AutoAugment as a highly efficient search algorithm to find the best augmentation policies. We use a greedy approach to reduce the exponential growth of the number of possible trials to linear growth. The Greedy Search also helps us to lead the search towards the sub-policies with better results, which eventually helps to increase the accuracy. The proposed method can be used as a reliable addition to the current artifitial neural networks. Our experiments on four datasets (Tiny ImageNet, CIFAR-10, CIFAR-100, and SVHN) show that Greedy AutoAugment provides better accuracy, while using 360 times fewer computational resources. [ABSTRACT FROM AUTHOR]

• A supervised classification based MRF model is proposed for depth map up-sampling. • Low-resolution depth segmentation is used to supervise color image classification. • Classification result is further introduced in the design of MRF energy function. • The designed MRF energy function is optimized with a gradient descent algorithm. • Comparisons with the state-of-the-art show the superiority of the proposed SC-MRF model. In this paper, a Supervised Classification assisted Markov Random Field (SC-MRF) model is proposed for generating high-quality up-sampled depth maps. The proposed model aims to reduce depth bleeding and depth confusion artifacts that can be produced at boundary regions of the up-sampled depth maps. In the proposed model, segmentation of low-resolution (LR) depth map is first used to supervise the classification of corresponding high-resolution (HR) color image. With this supervised classification, not only can the depth edges be retained, but redundant textures in the HR color image can be omitted. The classification result is then introduced into the design of a MRF energy function, and the final up-sampled depth map is obtained by optimizing this energy function with the gradient descent algorithm. For simplicity, classical K -means clustering is adopted to segment the LR depth map into several classes, and a feature-based K -nearest neighbour (K -NN) method is utilized for the supervised classification. With the proposed SC-MRF model, interaction between depths of different classes will be strongly suppressed, meaning depth edges are well preserved. Comparisons with the state-of-the-art demonstrate the strong performance of the proposed method both visually and by quantitative evaluation. [ABSTRACT FROM AUTHOR]

The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized spatial information. To this end, in this paper, novel gradient-based marker selection technique, dissimilarity measures, and adaptive connection weighting method are proposed by making full use of spatial information in hyperspectral image. Concretely, for a given hyperspectral image, a pixel-wise classification is firstly performed, and meanwhile the gradient map is generated by a morphology-based algorithm. Secondly, the most reliable pixels are selected as the markers from the classification map, and then the boundary noise pixels are excluded from the marker map by using the gradient map. Thirdly, several new dissimilarity measures are proposed by incorporating gradient information or probability information of pixels. Furthermore, in the growth procedure of MSF, the connection weighting between pixels is adjusted adaptively to improve the robustness of the MSF algorithm. Finally, when building the final classification map by using the majority voting rule, the labels of the training samples are used to dominate the label prediction. Experimental results are performed on two hyperspectral image sets Indian Pines and University of Pavia with different resolutions and contexts. The proposed approach yields higher classification accuracies compared to previously proposed classification methods, and provides accurate segmentation maps. [ABSTRACT FROM AUTHOR]

In hyperspectral image (HSI) classification, a big challenge is the limited sample size with a relatively high feature dimension. Therefore, effective feature extraction of data is essential, which is desired to remove the redundancy as well as improve the discrimination. A huge number of methods have been proposed for HSI feature extraction. In recent years, deep learning-based feature extraction algorithms have shown their superiorities in various classification problems. Within them, deep PCA (DPCA) is a simple but efficient algorithm, which runs fast due to the absence of back-propagation. However, DPCA fails to provide satisfactory classification accuracies on HSI datasets. In this paper, we try to combine DPCA with residual-based multi-scale feature extraction and propose a residual deep PCA (RDPCA) feature extraction algorithm for HSI classification. It is a hierarchical approach consisting of multiple layers. Within each layer, PCA is utilized for layer-wise feature extraction, and the reconstruction residual is fed into the next layer. When the feature is passed deeper into the RDPCA network, finer details are mined. The layer-wise features are concatenated to form the final output feature. Furthermore, to enhance the ability of nonlinear feature extraction, we add activation functions between adjacent layers. Experimental results on real-world HSI datasets have shown the superiority of the proposed RDPCA over DPCA and PCA. [ABSTRACT FROM AUTHOR]

Despite the rapid improvement in dental health over the last few decades, a significant portion of our population continue seek dental care every year. Estimates show that 13% of adults seek dental care for dental infection or fatigue within four years. The Social and individual burden of this disease can be reduced by its early detection. However, the symptoms of teeth infection in the early stages are not clear, hence, it would be relatively difficult to predict teeth infections based solely on human skills and experience. Big Data (BD) technologies have a great potential in transforming dental care, as they have revolutionized other industries. In addition to reducing cost, they could save millions of lives and improve patient outcomes. This paper proposes a novel integrated prediction model that extracts hidden knowledge from radiographic datasets containing a large volume of dental X-ray images and utilizes this knowledge to predict dental infections. Initially, preprocessing techniques using morphological skeleton and mean approach is applied to eliminate noise and enhance the images. Next, Multi Scale Segmented Region (MSR) approach, Watershed Approach (WA), Sobel edge Detection (SD), Histogram based Segmentation (HS), Trainable Segmentation (TS), Dual Clustering (DC), and Fuzzy C-Means clustering (FCM) are examined for image segmentation and feature extraction. Among these methods, MSR was selected for feature extraction since it outperformed other methods in terms of accuracy, specificity, precision, recall and F1-score. Then, a set of neural network classifiers are trained to identify patterns in the extracted optimized features and predict dental infections. For this purpose, we have examined Bacterial Optimized Recurrent Neural Networks (BORNN), Deep Learning Neural Networks (DANN), Genetic Optimized Neural Networks (GONN) and Adaptive Neural Networks Algorithm (ADNN). BORNN have shown maximum accuracy and Roc value (98.1% and 0.92 respectively), and minimum error values (MSE = 0.189, MAE = 0.143). The output of the proposed integrated prediction model is fed into a dental robot who proceeds with the treatment process with high accuracy and minimum delay. The proposed prediction model was implemented using a big data analytics tool called Apache SAMOA and experimental results showed its correctness and effectiveness. [ABSTRACT FROM AUTHOR]

At present, most intrusion detection algorithm cannot achieve a good balance between intrusion detection rate and false positive rate, in order to effectively avoid such problems, this paper proposed an intrusion detection method based on ADEN. The method first initialized the parameters of the encoder part in the ADEN model by training the deep belief network, and initialized the parameters of the decoder part by using the normal distribution. Then it tuned the parameters of the asymmetric deep belief network model by calculating the reconstruction error, so that the model can obtain the optimal low-dimensional representation of the original data. Finally, it used the data obtained by the encoder as input data of the classifier and detected. The ADEN model can extract features that are more conducive to classification and save more test time in the model initialization phase. The experimental results show that the method can achieve better detection performance and achieve better detection accuracy for small categories of samples. [ABSTRACT FROM AUTHOR]

Hyperspectral image classification is a challenging problem for machine learning methods due to the small number of labelled samples and high spectral variability. In this paper, to solve this problem, a novel superpixel and multi-classifier fusion (SMCF)-based classification method for hyperspectral images is proposed. This method takes full advantage of the spectral information of superpixels and the spatial information of hyperspectral images and includes the following three steps. First, superpixels are used to increase the number of training samples and their spectral diversity. Second, label propagation (LP) is used to classify the hyperspectral images. Although LP is an efficient semi-supervised classification method, the corresponding performance is poor for certain land cover types with dispersed spatial distributions. Thus, a support vector machine (SVM) classifier is introduced to classify the hyperspectral images. Finally, the results of the SVM and LP classifiers are combined using our new class-specific weighted fusion algorithm. In the experiments, we selected three widely used and real hyperspectral data sets for evaluation. The final classification performance was evaluated based on two common metrics: the overall accuracy (OA) and the Kappa coefficient. The experimental results show that the proposed SMCF method is superior to six well-known classification methods, even when only 1% or less of the labelled samples are used. [ABSTRACT FROM AUTHOR]

An unsupervised feature extraction method could effectively address the phenomenon where using a limited number of labelled samples leads to poor hyperspectral image (HSI) classification. Inspired by this method, we have developed a new method based on fusing dual unsupervised features for HSI classification in this paper. First, we used principal component analysis (PCA) to reduce the dimensions of HSI data and obtain the first set of unsupervised features. Then, we got the second set of unsupervised features by adopting the self-taught learning method, which could learn more discriminatory features by making full use of the information of unlabelled samples in the same HSI. Next, we used the correlation canonical analysis (CCA) algorithm to fuse linearly dual unsupervised learning features. Finally, we proved the effectiveness of the features extracted by the proposed algorithm by applying the extreme learning machine (ELM) classifier to evaluate the purposes. Experiments with three widely used real HSI datasets showed good classification performance when the number of training samples was quite limited. This demonstrates that the proposed feature learning method was indeed an effective method for HSI classification. [ABSTRACT FROM AUTHOR]

This paper investigates event extraction and early event classification in contiguous spatio-temporal data streams, where events need to be classified using partial information, i.e. while the event is ongoing. The framework incorporates an event extraction algorithm and an early event classification algorithm. We apply this framework to synthetic and real problems and demonstrate its reliability and broad applicability. The algorithms and data are available in the R package eventstream, and other code in the supplementary material. [ABSTRACT FROM AUTHOR]

The aim of this paper is to provide and numerically test in the presence of measurement noise a procedure for target classification in wave imaging based on comparing frequency-dependent distribution descriptors with precomputed ones in a dictionary of learned distributions. Distribution descriptors for inhomogeneous objects are obtained from the scattering coefficients. First, we extract the scattering coefficients of the (inhomogeneous) target from the perturbation of the reflected waves. Then, for a collection of inhomogeneous targets, we build a frequency-dependent dictionary of distribution descriptors and use a matching algorithm in order to identify a target from the dictionary up to some translation, rotation and scaling. [ABSTRACT FROM AUTHOR]

This paper presents a phasor-distance based faulty phase detection and fault classification technique for parallel transmission lines. Detection and classification of faulty phase(s) have been carried out by deriving indices from the change in phasor values of current with a distance of one cycle. The derived indices have zero values during normal operating conditions whereas the index corresponding to the faulty phase exceeds the pre-defined threshold in case of occurrence of a fault. A separate ground detection algorithm has been utilized for the identification of involvement of ground in a faulty situation. The performance of the proposed technique has been evaluated for intra-circuit, inter-circuit and simultaneous faults with wide variations in system and fault conditions. The suggested technique has been evaluated for over 23,000 diversified simulated fault cases as well as 14 recorded real fault events. The performance of the proposed technique remains consistent under Current Transformer (CT) saturation as well as different amount and direction of power flow. Moreover, suitability to different power system network has also been studied. Also, faults having fault current less than pre-fault conditions have been detected accurately. The results obtained suggest that it is able to detect faulty phases as well as classify faults within quarter-cycle from the inception of fault with impeccable accuracy. Besides, as modern digital relays have been already equipped with phasor computation facility, phasor-based technique can be easily incorporated with relative ease. At last, a comparative evaluation suggests its superiority in terms of fault classification accuracy, fault detection time, diversify fault scenarios and computational requirement among other existing techniques. [ABSTRACT FROM AUTHOR]

In order to overcome the problems of poor accuracy and high complexity of current classification algorithm for non-equilibrium data set, this paper proposes a decision tree classification algorithm for non-equilibrium data set based on random forest. Wavelet packet decomposition is used to denoise non-equilibrium data, and SNM algorithm and RFID are combined to remove redundant data from data sets. Based on the results of data processing, the non-equilibrium data sets are classified by random forest method. According to Bootstrap resampling method with certain constraints, the majority and minority samples of each sample subset are sampled, CART is used to train the data set, and a decision tree is constructed. Obtain the final classification results by voting on the CART decision tree classification. Experimental results show that the proposed algorithm has the characteristics of high classification accuracy and low complexity, and it is a feasible classification algorithm for non-equilibrium data set. [ABSTRACT FROM AUTHOR]

Alcohol is widely used in various fields today. Alcohol overdose and its improper use can cause many health problems. Drinking liquids with high alcohol concentration, especially methanol, causes poisoning, and the use of colognes with high alcohol concentration causes various skin conditions and respiratory diseases. Therefore, for public health it is important to determine the type and concentration of alcohol in beverages and liquid products. In this paper, K* algorithm is used to detect and classify liquids containing high concentration of alcohol by measuring the scattering parameter of the liquids in the microwave frequency band. For this purpose, scatter parameter values of aqueous solutions of ethanol, methanol, 1-propanol and 2-propanol at different concentrations have been used as training sets. Commercial liquids with known ethanol concentrations and liquids with other known alcohol concentrations have been tested to prove the accuracy of the proposed approach. As the results show the proposed approach can classify liquids containing alcohol and their alcohol concentrations successfully and with high accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a real-time ASL recognition system was built with a ConvNet algorithm using real colouring images from a PC camera. The model is the first ASL recognition model to categorize a total of 26 letters, including (J & Z), with two new classes for space and delete, which was explored with new datasets. It was built to contain a wide diversity of attributes like different lightings, skin tones, backgrounds, and a wide variety of situations. The experimental results achieved a high accuracy of about 98.53% for the training and 98.84% for the validation. As well, the system displayed a high accuracy for all the datasets when new test data, which had not been used in the training, were introduced. [ABSTRACT FROM AUTHOR]

Feature selection is one of the hottest machine learning topics in recent years. The main purposes of it are to simplify the original model, improve the readability of the model, and prevent over-fitting by searching for a suitable subset of features. There are many methods for this problem, including evolutionary algorithms and particle swarm optimization. Among them, the competitive swarm optimizer is a new optimization algorithm proposed in recent years, which is based on particle swarm optimization algorithm, and has achieved good results in high-dimensional feature selection problems, but it also has the problems of high computation time cost and easily being premature. Aiming at these problems, this paper proposes to add the crossover operator and mutation operator in the genetic algorithm to the competitive swarm optimization, so as to improve the generation speed of new individuals in the algorithm and prevent premature population. After testing on UC Irvine Machine Learning Repository, the new algorithm not only improves the computational efficiency, but also avoids the problem that the competitive swarm optimization algorithm is easy to fall into the local optimum, which greatly improves the calculation effect. [ABSTRACT FROM AUTHOR]

The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of $\ell 1$ minimization and works well for image recognition satisfying a subspace assumption. In this paper we propose a new implementation of SRC via screening, establish its equivalence to the original SRC under regularity conditions, and prove its classification consistency under a latent subspace model and contamination. The results are demonstrated via simulations and real data experiments, where the new algorithm achieves comparable numerical performance and significantly faster. [ABSTRACT FROM AUTHOR]

Background. Currently, echocardiography has become an essential technology for the diagnosis of cardiovascular diseases. Accurate classification of apical two-chamber (A2C), apical three-chamber (A3C), and apical four-chamber (A4C) views and the precise detection of the left ventricle can significantly reduce the workload of clinicians and improve the reproducibility of left ventricle segmentation. In addition, left ventricle detection is significant for the three-dimensional reconstruction of the heart chambers. Method. RetinaNet is a one-stage object detection algorithm that can achieve high accuracy and efficiency at the same time. RetinaNet is mainly composed of the residual network (ResNet), the feature pyramid network (FPN), and two fully convolutional networks (FCNs); one FCN is for the classification task, and the other is for the border regression task. Results. In this paper, we use the classification subnetwork to classify A2C, A3C, and A4C images and use the regression subnetworks to detect the left ventricle simultaneously. We display not only the position of the left ventricle on the test image but also the view category on the image, which will facilitate the diagnosis. We used the mean intersection-over-union (mIOU) as an index to measure the performance of left ventricle detection and the accuracy as an index to measure the effect of the classification of the three different views. Our study shows that both classification and detection effects are noteworthy. The classification accuracy rates of A2C, A3C, and A4C are 1.000, 0.935, and 0.989, respectively. The mIOU values of A2C, A3C, and A4C are 0.858, 0.794, and 0.838, respectively. [ABSTRACT FROM AUTHOR]

The emerging of the Internet of things (IoT), and more, the advent of the Internet of everything have revolutionized the computer networks industry. The high diversity of IoT devices, its protocols and standards, and its limited computational resources have led to the appearance of novel security challenges. Hence, the traditional security countermeasures of encryption and authentication are insufficient. Promoting the network security is a fundamental concern for practitioners for safeguarding their economical and industrial strategies. Intrusion detection systems (IDSs) are the major solutions for protecting Internet-connected frameworks at the network-level. But, more importantly, is how to convert the traditional IDSs into intelligent IDSs that resemble the intelligent IoT. This paper presents a new approach for converting the traditional IDSs into smart, evolutionary, and multi-objective IDSs for IoT networks. Moreover, this article presents a modified algorithm for IDSs that tackles the problem of feature selection. The modified algorithm stands on the integration of multi-objective particle swarm optimization with Lévy flight randomization component (MOPSO-Lévy); the modified MOPSO-Lévy has been tested on real IoT network data that is drawn from UCI repository. MOPSO-Lévy has achieved superior performance results when compared with state-of-the-art evolutionary multi-objective algorithms. [ABSTRACT FROM AUTHOR]

Due to the lack of experience and prior knowledge, the selection of the most informative features has become one of the challenging problems in many applications. Recently, many metaheuristic algorithms have widely used to solve the feature selection problem for classification tasks. In this paper, the chaotic atom search optimization (CASO) that integrates the chaotic maps into atom search optimization (ASO) is applied for wrapper feature selection. Twelve different chaotic maps are used to adjust the parameter of CASO through the optimization process, which is beneficial for enhancing the convergence rate and improving the efficiency of ASO algorithm. In this study, twenty benchmark datasets acquired from the UCI machine learning repository are used to validate the performance of CASO in feature selection. Several state-of-the-art metaheuristic algorithms are adopted to examine the efficacy and effectiveness of the proposed approach. Our results indicated that the Logistic-Tent map was the most suitable chaotic map to boost the performance of CASO. The experimental result shows the capability of CASO not only in finding the optimal solution but also in significantly improving the prediction accuracy and reducing the number of features. [ABSTRACT FROM AUTHOR]

Nowadays Data mining has been broadly used in medical databases. The k-nearest neighbors (knn) algorithm is a more popular and effi- cient algorithm for pattern recognition. Nowadays a lot of people are sick with Chronic Kidney Disease (CKD). In this paper, data analysis on CKD Dataset is done using k-nearest neighbours algorithm. [ABSTRACT FROM AUTHOR]