Effective feature selection using feature vector graph for classification

Zhao, Guodong; Wu, Yan; Chen, F.; Zhang, Junming; Bai, Jing

doi:10.1016/j.neucom.2014.09.027

Cited by 27 publications

(10 citation statements)

References 35 publications

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“…The precision in this present work is of 10 times accuracy average. Five filter methods and two wrapped methods are compared with the method used in this present study, and these include the CMQFS 30 , the mRMR, the MIFS-U, the CMIM, the Relief 31 , the SVMRFE 4 , and the KNNFS 32 methods.…”

Section: Resultsmentioning

confidence: 99%

“…The key reason is that mRMR not only considers the relevance of genes, but it also considers the redundancy between genes. However, the mRMR method only measures the quantity of irrelevant redundancy ( IR ) 30 , but does not deal with its relevant redundancy ( RR ) 30 . This can cause a problem since this method chooses some irrelevant variables prematurely, and is delayed in picking out some useful variables 39 40 .…”

Section: Resultsmentioning

confidence: 99%

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Feature Subset Selection for Cancer Classification Using Weight Local Modularity

Zhao

2016

Sci Rep

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Microarray is recently becoming an important tool for profiling the global gene expression patterns of tissues. Gene selection is a popular technology for cancer classification that aims to identify a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers to obtain a high predictive accuracy. This technique has been extensively studied in recent years. This study develops a novel feature selection (FS) method for gene subset selection by utilizing the Weight Local Modularity (WLM) in a complex network, called the WLMGS. In the proposed method, the discriminative power of gene subset is evaluated by using the weight local modularity of a weighted sample graph in the gene subset where the intra-class distance is small and the inter-class distance is large. A higher local modularity of the gene subset corresponds to a greater discriminative of the gene subset. With the use of forward search strategy, a more informative gene subset as a group can be selected for the classification process. Computational experiments show that the proposed algorithm can select a small subset of the predictive gene as a group while preserving classification accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Feature Subset Selection for Cancer Classification Using Weight Local Modularity

Zhao

2016

Sci Rep

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“…Modularity Qvalue-Based Feature Selection (CMQFS) model is developed using two methods. First one is mutual information-based criterion and second one relevant independency between features [45]. Kernelized fuzzy rough sets (KFRS) and the Memetic algorithm (MA) is hybridized for transient stability assessment of power systems.…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Feature Selection Algorithms

Kumar¹

2015

IJRITCC

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Abstract-One major component of machine learning is feature analysis which comprises of mainly two processes: feature selection and fe ature extraction. Due to its applications in several areas including data mining, soft computing and big data analysis, feature selection has got a reasonable importance. This paper presents an introductory concept of feature selection with various inherent approaches. The paper surveys historic developments reported in feature selection with supervised and unsupervised methods. The recent developments with the state of the art in the on-going feature selection algorithms have also been summarized in the paper including their hybridizations.

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“…Classification are fundamental to many theoretical and practical applications, including pattern recognition [2][3][4], fault diagnosis [5][6][7], and image processing [8][9][10], etc. Many well-known methods have been proposed to solve classification problems, including k nearest neighbors (k-NN) [11], support vector machine [12], naive Bayes [13], Bayes net [14], decision tree learner [15], random forest [16], and other latest techniques, such as gravitational inspired classifier [17], feature vector graph-based classifier [18], and Learning Automata(LA)-based classifier [19] , and so on.…”

Section: Introductionmentioning

confidence: 99%

Data classification using evidence reasoning rule

Zheng

Yang

et al. 2017

Knowledge-Based Systems

181

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Abstract-In Dempster-Shafer evidence theory (DST) based classifier design, Dempster's combination (DC) rule is commonly used as a multi-attribute classifier to combine evidence collected from different attributes. The main aim of this paper is to present a classification method using a novel combination rule i.e., the evidence reasoning (ER) rule. As an improvement of the DC rule, the newly proposed ER rule defines the reliability and weight of evidence. The former indicates the ability of attribute or its evidence to provide correct assessment for classification problem, and the latter reflects the relative important of evidence in comparison with other evidence when they need to be combined. The ER rule-based classification procedure is expatiated from evidence acquisition and estimation of evidence reliability and weight to combination of evidence. It is a purely data-driven approach without making any assumptions about the relationships between attributes and class memberships, and the specific statistic distributions of attribute data. Experiential results on five popular benchmark databases taken from University of California Irvine (UCI) machine learning database show high classification accuracy that is competitive with other classical and mainstream classifiers.

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Effective feature selection using feature vector graph for classification

Cited by 27 publications

References 35 publications

Feature Subset Selection for Cancer Classification Using Weight Local Modularity

Feature Subset Selection for Cancer Classification Using Weight Local Modularity

A Survey on Feature Selection Algorithms

Data classification using evidence reasoning rule

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