Feature Selection and Classification in gene expression cancer data

被引：0

作者：

Pavithra, D. ^{[1
]}

Lakshmanan, B. ^{[1
]}

机构：

[1] MepcoSchlenk Engn Coll, Dept Comp Sci & Engn, Sivakasi, Tamil Nadu, India

来源：

2017 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN DATA SCIENCE (ICCIDS) | 2017年

关键词：

Gene expression Data; feature selection; Genetic approach; Classification; MUTUAL INFORMATION; RELEVANCE;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Gene Expression data comprising more number genes and samples are used for cancer classification. Gene Expression information provides the gene expression level contributing to specific action. Cancer classification is done using the gene expression data. In our work, feature selection methods such as mutual information and genetic algorithm are used for examining cancer microarray Gene expression Data. By using feature selection methods most probable cancer associated genes are selected from large microarray Gene Expression data. The objective of the present work to attain enhanced classification accuracy. We have used the colon cancer dataset contains 2000 gene has 62 types, in which 40 are colon cancer tissue's types and the other 20 are normal types.

引用

页数：6

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