title: Recursive feature selection with significant variables of support vectors.
abstract: The development of DNA microarray makes researchers screen thousands of genes simultaneously and it also helps determine high- and low-expression level genes in normal and disease tissues. Selecting relevant genes for cancer classification is an important issue. Most of the gene selection methods use univariate ranking criteria and arbitrarily choose a threshold to choose genes. However, the parameter setting may not be compatible to the selected classification algorithms. In this paper, we propose a new gene selection method (SVM-t) based on the use of t-statistics embedded in support vector machine. We compared the performance to two similar SVM-based methods: SVM recursive feature elimination (SVMRFE) and recursive support vector machine (RSVM). The three methods were compared based on extensive simulation experiments and analyses of two published microarray datasets. In the simulation experiments, we found that the proposed method is more robust in selecting informative genes than SVMRFE and RSVM and capable to attain good classification performance when the variations of informative and noninformative genes are different. In the analysis of two microarray datasets, the proposed method yields better performance in identifying fewer genes with good prediction accuracy, compared to SVMRFE and RSVM.
authors.affiliation: Department of Agronomy, National Taiwan University, Taipei 106, Taiwan. catsai@ntu.edu.tw
authors.forename: Chen-An
authors.initials: CA
authors.lastname: Tsai
authors.forename: Chien-Hsun
authors.initials: CH
authors.lastname: Huang
authors.forename: Ching-Wei
authors.initials: CW
authors.lastname: Chang
authors.forename: Chun-Houh
authors.initials: CH
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D016208:Databases, Factual; D030541:Databases, Genetic; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D060388:Support Vector Machine