Michigan Style Fuzzy Classification for Gene Expression Analysis

摘要

Interest in microarray studies and gene expression analysis is growing as they are likely to provide promising avenues towards the understanding of fundamental questions in biology and medicine. In this paper we employment a hybrid fuzzy rule-based classification system for effective analysis of gene expression data. Our classifier consists of a set of fuzzy if-then rules that allows for accurate non-linear classification of input patterns. A small number of fuzzy if-then rules are selected through means of a genetic algorithm in order to provide a compact classifier for gene expression analysis. Experimental results on various well-known gene expression datasets confirm the efficacy of the presented approach.