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
收藏
页码：422 / +
页数：2
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