Classifying gene expression data of cancer using classifier ensemble with mutually exclusive features

Sung Bae Cho, Jungwon Ryu

Research output: Contribution to journalArticle

70 Citations (Scopus)

Abstract

The explosion of DNA and protein sequence data in public and private databases has been encouraging interdisciplinary research on biology and information technology. Gene expression profiles are just sequences of numbers, and the necessity of tools analyzing them to get useful information has risen significantly. In order to predict the cancer class of patients from the gene expression profile, this paper presents a classification framework that combines a pair of classifiers trained with mutually exclusive features. The idea behind feature selection with nonoverlapping correlation is to encourage classifier ensemble, which consists of multiple classifiers, to leam different aspects of training data, so that classifiers can search in a wide solution space. Experimental results show that the classifier ensemble produces higher recognition accuracy than conventional classifiers.

Original languageEnglish
Pages (from-to)1744-1753
Number of pages10
JournalProceedings of the IEEE
Volume90
Issue number11
DOIs
Publication statusPublished - 2002

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

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