title: A centroid-based gene selection method for microarray data classification.
abstract: For classification problems based on microarray data, the data typically contains a large number of irrelevant and redundant features. In this paper, a new gene selection method is proposed to choose the best subset of features for microarray data with the irrelevant and redundant features removed. We formulate the selection problem as a L1-regularized optimization problem, based on a newly defined linear discriminant analysis criterion. Instead of calculating the mean of the samples, a kernel-based approach is used to estimate the class centroid to define both the between-class separability and the within-class compactness for the criterion. Theoretical analysis indicates that the global optimal solution of the L1-regularized criterion can be reached with a general condition, on which an efficient algorithm is derived to the feature selection problem in a linear time complexity with respect to the number of features and the number of samples. The experimental results on ten publicly available microarray datasets demonstrate that the proposed method performs effectively and competitively compared with state-of-the-art methods.
authors.affiliation: Department of Electronic Engineering, Xiamen University, Fujian 361005, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.
authors.forename: Shun
authors.initials: S
authors.lastname: Guo
authors.affiliation: Department of Electronic Engineering, Xiamen University, Fujian 361005, China. Electronic address: shun.guo@siat.ac.cn.
authors.forename: Donghui
authors.initials: D
authors.lastname: Guo
authors.affiliation: School of Mathematics and Computer Science, Fujian Normal University, Fujian 350117, China.
authors.forename: Lifei
authors.initials: L
authors.lastname: Chen
authors.affiliation: Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518000, China.
authors.forename: Qingshan
authors.initials: Q
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D000818:Animals; D014408:Biomarkers, Tumor / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016015:Logistic Models; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
chemical_list: D014408:Biomarkers, Tumor
keywords: Class centroid; Classification; Gene selection; L1 regularization; Microarray data