EXPERIMENTAL STUDY ON PERFORMANCE OF SYMBOLIC CLASSIFIER WITH GENE SELECTION METHODS FOR MULTICLASS MICROARRAY GENE EXPRESSION DATA

Abstract

Microarray is a useful technique for measuring expression data of thousands of genes simultaneously. The expression level of genes is known to contain the keys to address fundamental problems relating to the prevention and cure of diseases, biological evolution mechanisms and drug discovery. Previous research has demonstrated that this technology can be useful in the classification of cancers. Most proposed cancer classification methods work well only on binary class problems and not extensible to multi-class problems. This work is an attempt to classify high dimensional, multiclass Microarray Gene expression data using symbolic classifier.