Feature selection via kernel sparse representation

With the continuous development of Internet technology, data gradually present a complicated and high-dimensional trend. These high-dimensional data have a large number of redundant features and irrelevant features, which bring great challenges to the existing machine learning algorithms. Feature selection is one of the important research topics in the fields of machine learning, pattern recognition and data mining, and it is also an important means in the data preprocessing stage. Feature selection is to look for the optimal feature subset from the original feature set, which would improve the classification accuracy and reduce the machine learning time. The traditional feature selection algorithm tends to ignore the kind of feature which has a weak distinguishing capacity as a monomer, whereas the feature group’s distinguishing capacity is strong. Therefore, a new dynamic interaction feature selection (DIFS) algorithm is proposed in this paper. Initially, under the theoretical framework of interactive information, it redefines the relevance, irrelevance and redundancy of the features. Secondly, it offers the computational formulas for calculating interactive information. Finally, under the eleven data sets of UCI and three different classifiers, namely, KNN, SVM and C4.5, the DIFS algorithm increases the classification accuracy of the FullSet by 3.2848% and averagely decreases the number of features selected by 15.137. Hence, the DIFS algorithm can not only identify the relevance feature effectively, but also identify the irrelevant and redundant features. Moreover, it can effectively improve the classification accuracy of the data sets and reduce the feature dimensions of the data sets.

Based on benchmarking various methods and analyzing multiple real scRNA-seq datasets, a computational platform/workflow and a set of tips for best practices are developed for analyzing causal interactions or relationships in single cells.

Graph self-representation method for unsupervised feature selection

In the present state of affairs, the motive behind every educational organization is to uplift the academic achievement of students. Educational data mining (EDM) is an upward field of research, and it is very helpful for academic institutions to predict the academic performance of the students. Educational datasets are the basis of various predictive models. Quality of these models can be improved by using feature selection (FS). To get the required benefits from the available data, there must be some tools for analysis and prediction. In lieu of the above, machine learning/data mining are most suitable. In educational data mining, for better accuracy of prediction models’ and quality of various educational datasets, feature selection (FS) plays a vital role. Feature selection (FS) algorithms abolish inappropriate information from the repositories of educational background so that performance of classifier in terms of accuracy could be increased and the same could be used for better decision. In lieu of the above, a best feature selection algorithm must be selected. In this paper, two filter selection approaches namely correlation feature selection (CFS) and wrapper-based feature selection have been used to demonstrate the importance of selection of a feature subset for a classification problem. The present paper aims to find the detailed investigation of filter feature selection algorithms along with the classification algorithms on a given dataset. We found result with numerous numbers of features from various Feature selection algorithms and classifiers which will help the researcher to discover the most excellent mixture of filter feature selection algorithms and its associated classifiers. The result indicates that SMO and J48 have the highest accuracy measures with the correlation feature selection algorithms, while Naïve Bayes has the highest accuracy measures with the wrapper subset feature selection algorithms for predicting high, medium and low grade for the students.

Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks.

Many feature selection algorithms have been proposed in the past focusing on improving classification accuracy. In this work, we point out the importance of stable feature selection for knowledge discovery from high-dimensional data, and identify two causes of instability of feature selection algorithms: selection of a minimum subset without redundant features and small sample size. We propose a general framework for stable feature selection which emphasizes both good generalization and stability of feature selection results. The framework identifies dense feature groups based on kernel density estimation and treats features in each dense group as a coherent entity for feature selection. An efficient algorithm DRAGS (Dense Relevant Attribute Group Selector) is developed under this framework. We also introduce a general measure for assessing the stability of feature selection algorithms. Our empirical study based on microarray data verifies that dense feature groups remain stable under random sample hold out, and the DRAGS algorithm is effective in identifying a set of feature groups which exhibit both high classification accuracy and stability.

With the growth of Internet, there has been a tremendous increases in the number of attacks and therefore Intrusion Detection Systems (IDS’s) has become a main stream of information security. The purpose of IDS is to help the computer systems to deal with attacks. This anomaly detection system creates a database of normal behaviour and deviations from the normal behaviour to trigger during the occurrence of intrusions. Based on the source of data, IDS is classified into Host based IDS and Network based IDS. In network based IDS, the individual packets flowing through the network are analyzed where as in host based IDS the activities on the single computer or host are analyzed. The feature selection used in IDS helps to reduce the classification time. In this paper, the IDS for detecting the attacks effectively has been proposed and implemented. For this purpose, a new feature selection algorithm called Optimal Feature Selection algorithm based on Information Gain Ratio has been proposed and implemented. This feature selection algorithm selects optimal number of features from KDD Cup dataset. In addition, two classification techniques namely Support Vector Machine and Rule Based Classification have been used for effective classification of the data set. This system is very efficient in detecting DoS attacks and effectively reduces the false alarm rate. The proposed feature selection and classification algorithms enhance the performance of the IDS in detecting the attacks.

<p>Classification problems often have a large number of features, but not all of them are useful for classification. Irrelevant and redundant features may even reduce the classification accuracy. Feature selection is a process of selecting a subset of relevant features, which can decrease the dimensionality, shorten the running time, and/or improve the classification accuracy. There are two types of feature selection approaches, i.e. wrapper and filter approaches. Their main difference is that wrappers use a classification algorithm to evaluate the goodness of the features during the feature selection process while filters are independent of any classification algorithm. Feature selection is a difficult task because of feature interactions and the large search space. Existing feature selection methods suffer from different problems, such as stagnation in local optima and high computational cost. Evolutionary computation (EC) techniques are well-known global search algorithms. Particle swarm optimisation (PSO) is an EC technique that is computationally less expensive and can converge faster than other methods. PSO has been successfully applied to many areas, but its potential for feature selection has not been fully investigated. The overall goal of this thesis is to investigate and improve the capability of PSO for feature selection to select a smaller number of features and achieve similar or better classification performance than using all features. This thesis investigates the use of PSO for both wrapper and filter, and for both single objective and multi-objective feature selection, and also investigates the differences between wrappers and filters. This thesis proposes a new PSO based wrapper, single objective feature selection approach by developing new initialisation and updating mechanisms. The results show that by considering the number of features in the initialisation and updating procedures, the new algorithm can improve the classification performance, reduce the number of features and decrease computational time. This thesis develops the first PSO based wrapper multi-objective feature selection approach, which aims to maximise the classification accuracy and simultaneously minimise the number of features. The results show that the proposed multi-objective algorithm can obtain more and better feature subsets than single objective algorithms, and outperform other well-known EC based multi-objective feature selection algorithms. This thesis develops a filter, single objective feature selection approach based on PSO and information theory. Two measures are proposed to evaluate the relevance of the selected features based on each pair of features and a group of features, respectively. The results show that PSO and information based algorithms can successfully address feature selection tasks. The group based method achieves higher classification accuracies, but the pair based method is faster and selects smaller feature subsets. This thesis proposes the first PSO based multi-objective filter feature selection approach using information based measures. This work is also the first work using other two well-known multi-objective EC algorithms in filter feature selection, which are also used to compare the performance of the PSO based approach. The results show that the PSO based multiobjective filter approach can successfully address feature selection problems, outperform single objective filter algorithms and achieve better classification performance than other multi-objective algorithms. This thesis investigates the difference between wrapper and filter approaches in terms of the classification performance and computational time, and also examines the generality of wrappers. The results show that wrappers generally achieve better or similar classification performance than filters, but do not always need longer computational time than filters. The results also show that wrappers built with simple classification algorithms can be general to other classification algorithms.</p>

This paper initiates perceptions and algorithms of feature selection, survey of existing feature selection algorithms and assesses diverse algorithms with a classifying frame based on search approaches, valuation criteria, and provides strategy in selecting feature selection algorithms. A unifying platform is projected to continue our efforts headed for building an incorporated system for intelligent feature selection. Feature selection intends to reduce the dimensionality of patterns for classification by choosing the most informative instead of irrelevant and/or redundant features. In this proposed work Intuitionistic fuzzy based feature clustering is proposed for grouping features based on the degree of membership and degree of indeterminacy among the attributes and clusters. In this proposed work a novel approach which uses an Intuitionistic fuzzy version of k-means has been introduced for grouping interdependent features. Genetic algorithm reinstating which is the variation of traditional genetic algorithm is then applied to appraise whether the measured feature is independent of class labels; hence, it leads to eliminate unrelated clusters to classification process and progress the selection of features. The proposed method achieves improvement on classification accuracy and perhaps to select less number of features which show the way to simplification of learning task to a big extent. The Experiment results have been demonstrated by the good performance and also find good enough subset features of this method on using UCI benchmark datasets that are for data mining methods such as Breast Cancer, Sensor and Iris Records.

High dimension and complexity of network high-dimensional data lead to poor feature selection effect network high-dimensional data. To effectively solve this problem, feature selection algorithms for high-dimensional network data based on supervised discriminant projection (SDP) have been designed. The sparse representation problem of high-dimensional network data is transformed into an Lp norm optimization problem, and the sparse subspace clustering method is used to cluster high-dimensional network data. Dimensionless processing is carried out for the clustering processing results. Based on the linear projection matrix and the best transformation matrix, the dimensionless processing results are reduced by combining the SDP. The sparse constraint method is used to achieve feature selection of high-dimensional data in the network, and the relevant feature selection results are obtained. The experimental findings demonstrate that the suggested algorithm can effectively cluster seven different types of data and converges when the number of iterations approaches 24. The F1 value, recall, and precision are all kept at high levels. High-dimensional network data feature selection accuracy on average is 96.9%, and feature selection time on average is 65.1 milliseconds. The selection effect for network high-dimensional data features is good.

Feature (or variable) selection is the process of identifying the minimal set of features with the highest predictive performance on the target variable of interest. Numerous feature selection algorithms have been developed over the years, but only few have been implemented in R and made publicly available R as packages while offering few options. The R package MXM offers a variety of feature selection algorithms, and has unique features that make it advantageous over its competitors: a) it contains feature selection algorithms that can treat numerous types of target variables, including continuous, percentages, time to event (survival), binary, nominal, ordinal, clustered, counts, left censored, etc; b) it contains a variety of regression models that can be plugged into the feature selection algorithms (for example with time to event data the user can choose among Cox, Weibull, log logistic or exponential regression); c) it includes an algorithm for detecting multiple solutions (many sets of statistically equivalent features, plain speaking, two features can carry statistically equivalent information when substituting one with the other does not effect the inference or the conclusions); and d) it includes memory efficient algorithms for high volume data, data that cannot be loaded into R (In a 16GB RAM terminal for example, R cannot directly load data of 16GB size. By utilizing the proper package, we load the data and then perform feature selection.). In this paper, we qualitatively compare MXM with other relevant feature selection packages and discuss its advantages and disadvantages. Further, we provide a demonstration of MXM’s algorithms using real high-dimensional data from various applications.

<p>Classification aims to identify a class label of an instance according to the information from its characteristics or features. Unfortunately, many classification problems have a large feature set containing irrelevant and redundant features, which reduce the classification performance. In order to address the above problem, feature selection is proposed to select a small subset of relevant features. There are three main types of feature selection methods, i.e. wrapper, embedded and filter approaches. Wrappers use a classification algorithm to evaluate candidate feature subsets. In embedded approaches, the selection process is embedded in the training process of a classification algorithm. Different from the other two approaches, filters do not involve any classification algorithm during the selection process. Feature selection is an important process but it is not an easy task due to its large search space and complex feature interactions. Because of the potential global search ability, Evolutionary Computation (EC), especially Particle Swarm Optimization (PSO), has been widely and successfully applied to feature selection. However, there is potential to improve the effectiveness and efficiency of EC-based feature selection. The overall goal of this thesis is to investigate and improve the capability of EC for feature selection to select small feature subsets while maintaining or even improving the classification performance compared to using all features. Different aspects of feature selection are considered in this thesis such as the number of objectives (single-objective/multi-objective), the fitness function (filter/wrapper), and the searching mechanism. This thesis introduces a new fitness function based on mutual information which is calculated by an estimation approach instead of the traditional counting approach. Results show that the estimation approach works well on both continuous and discrete data. More importantly, mutual information calculated by the estimation approach can capture feature interactions better than the traditional counting approach. This thesis develops a novel binary PSO algorithm, which is the first work to redefine some core concepts of PSO such as velocity and momentum to suit the characteristics of binary search spaces. Experimental results show that the proposed binary PSO algorithm evolve better solutions than other binary EC algorithms when the search spaces are large and complex. Specifically, on feature selection, the proposed binary PSO algorithm can select smaller feature subsets with similar or better classification accuracies, especially when there are a large number of features. This thesis proposes surrogate models for wrapper-based feature selection. The surrogate models use surrogate training sets which are subsets of informative instances selected from the training set. Experimental results show that the proposed surrogate models assist PSO to reduce the computational cost while maintaining or even improving the classification performance compared to using only the original training set. The thesis develops the first wrapper-based multi-objective feature selection algorithm using MOEA/D. A new decomposition strategy using multiple reference points for MOEA/D is designed, which can deal with different characteristics of multi-objective feature selection such as highly discontinuous Pareto fronts and complex relationships between objectives. The experimental results show that the proposed algorithm can evolve more diverse non-dominated sets than other multi-objective algorithms. This thesis introduces the first PSO-based feature selection algorithm for transfer learning. In the proposed algorithm, the fitness function uses classification performance to reduce the differences between domains while maintaining the discriminative ability on the target domain. The experimental results show that the proposed algorithm can select feature subsets which achieve better classification performance than four state-of-the-art feature-based transfer learning algorithms.</p>

BackgroundSuccessfully modeling high-dimensional data involving thousands of variables is challenging. This is especially true for gene expression profiling experiments, given the large number of genes involved and the small number of samples available. Random Forests (RF) is a popular and widely used approach to feature selection for such "small n, large p problems." However, Random Forests suffers from instability, especially in the presence of noisy and/or unbalanced inputs.ResultsWe present RKNN-FS, an innovative feature selection procedure for "small n, large p problems." RKNN-FS is based on Random KNN (RKNN), a novel generalization of traditional nearest-neighbor modeling. RKNN consists of an ensemble of base k-nearest neighbor models, each constructed from a random subset of the input variables. To rank the importance of the variables, we define a criterion on the RKNN framework, using the notion of support. A two-stage backward model selection method is then developed based on this criterion. Empirical results on microarray data sets with thousands of variables and relatively few samples show that RKNN-FS is an effective feature selection approach for high-dimensional data. RKNN is similar to Random Forests in terms of classification accuracy without feature selection. However, RKNN provides much better classification accuracy than RF when each method incorporates a feature-selection step. Our results show that RKNN is significantly more stable and more robust than Random Forests for feature selection when the input data are noisy and/or unbalanced. Further, RKNN-FS is much faster than the Random Forests feature selection method (RF-FS), especially for large scale problems, involving thousands of variables and multiple classes.ConclusionsGiven the superiority of Random KNN in classification performance when compared with Random Forests, RKNN-FS's simplicity and ease of implementation, and its superiority in speed and stability, we propose RKNN-FS as a faster and more stable alternative to Random Forests in classification problems involving feature selection for high-dimensional datasets.

Feature selection is a useful tool for identifying which features, or attributes, of a dataset cause or explain the phenomena that the dataset describes, and improving the efficiency and accuracy of learning algorithms for discovering such phenomena. Consequently, feature selection has been studied intensively in machine learning research. However, while feature selection algorithms that exhibit excellent accuracy have been developed, they are seldom used for analysis of high-dimensional data because high-dimensional data usually include too many instances and features, which make traditional feature selection algorithms inefficient. To eliminate this limitation, we tried to improve the run-time performance of two of the most accurate feature selection algorithms known in the literature. The result is two accurate and fast algorithms, namely sCwc and sLcc. Multiple experiments with real social media datasets have demonstrated that our algorithms improve the performance of their original algorithms remarkably. For example, we have two datasets, one with 15,568 instances and 15,741 features, and another with 200,569 instances and 99,672 features. sCwc performed feature selection on these datasets in 1.4 seconds and in 405 seconds, respectively. In addition, sLcc has turned out to be as fast as sCwc on average. This is a remarkable improvement because it is estimated that the original algorithms would need several hours to dozens of days to process the same datasets. In addition, we introduce a fast implementation of our algorithms: sCwc does not require any adjusting parameter, while sLcc requires a threshold parameter, which we can use to control the number of features that the algorithm selects.

New hybrid data mining model for credit scoring based on feature selection algorithm and ensemble classifiers