Using a grid computing-based meta-evolutionary mining approach for the microarray data cancer-categorization

Abstract

Purpose The categorization response model through gene expression patterns turns into one of the most favorable utilizations of the microarray technology. In this study, the aim is to propose a grid computing-based meta-evolutionary mining approach as a categorization response model for gene selection and cancer classification. Design/methodology/approach The proposed approach is based on the grid computing infrastructure for establishing the best attributes set selected from a big microarray data. The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy. Findings Examples for several benchmarking cancer microarray data sets were used to evaluate the proposed approach, whose results are also compared with other approaches in literatures. Experimental results from four benchmarking problems indicate that the proposed approach works effectively and efficiently, and the results of the proposed methods are superior to or as well as other existing methods in literatures. Originality/value The novel discriminant analysis is based on vector distant of median method as the evaluation function of meta-evolutionary mining approach to discover the best feature subset automatically from the microarray tumor database. In this study, the proposed approach lays stress on finding the best attributes set for constructing a categorization response model with highest categorization accuracy.