Majority Voting of Semantic Genetic Programming for Microarray data

Researchers have found different types of cancer cell along with various normal gene structures in Microarray data. It is possible to set benchmark for finding out affected cell from normal one using various machine learning technique. Due to wide range of gene about thousand of them and minimum training data there occurs imbalance between them. This difference can be minimized using various optimizing algorithm and machine learning technique. In this paper we proposed Combined Genetic Programming for Microarray Data along with Majority Voting(MV) for classification. Genetic program along with MV act as both classifier and gene selection. The Quantitative relationships exists among the more frequently selected genes and it has been improved using majority voting techniques. The potential challenge for genetic program is it has to find gene type and also has to find optimal solution from small number of training samples compared to huge number of genes.