Feature Selection for Cancer Classification Based on Support Vector Machine

被引：3

作者：

Luo, Wei ^{[1
]}

Wang, Lipo ^{[2
]}

Sun, Jingjing ^{[1
]}

机构：

[1] Xiangtan Univ, Coll Informat Engn, Xiangtan, Hunan, Peoples R China

[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore, Singapore

来源：

PROCEEDINGS OF THE 2009 WRI GLOBAL CONGRESS ON INTELLIGENT SYSTEMS, VOL IV | 2009年

关键词：

GENE-EXPRESSION DATA; PATTERNS;

D O I：

10.1109/GCIS.2009.45

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Feature selection plays an important role in cancer classification, for gene expression data usually have a large number of dimensions and relatively a small number of samples In this paper we use the support vector machine (SVM) for cancer classification. We propose a mixed two-step feature selection method The first step uses a modified t-test method to select discriminatory features The second step extracts principal components from the top-ranked genes based on the modified t-test method We tested our two-step method in three data sets, i e, the lymphoma data set, the SRBCT data set, and the ovarian cancer data set. The results in all the three data sets show our two-step methods is able to achieve 100% accuracy with much fewer genes than other published results

引用

页码：422 / +

页数：2

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