GT-kernelPLS: Game Theory Based Hybrid Gene Selection Method for Microarray Data Classification

Gene selection prior to classification has been an important topic in bioinformatics, since last decade. Small sample size and high dimensionality in microarray data pose great challenges for performing efficient classification. In this paper we propose efficient hybrid method (GT-kernelPLS) with a combination of wrapper like technique coalitional game theory and kernel partial least square (kernelPLS) filter method. Experimental results on ten microarray data sets ensure that GT-kernelPLS achieve higher accuracy than several state of the art feature selection methods, and it exhibits a reasonable execution time, even for the data sets having more than twenty thousand genes.