A Hybrid Cancer Classification Model Based Recursive Binary Gravitational Search Algorithm in Microarray Data

Abstract

Nowadays, in clinical medicine diagnosticians usually use DNA microarray datasets for diagnosis and classification of cancer. However, DNA microarray datasets typically have very large number of genes and less number of samples, therefore, before diagnosis and classification of cancer it is quite requisite to select most relevant genes. In this paper, we have developed a two phase classification model in which most relevant genes are selected by integrating ReliefF with Recursive Binary Gravitational Search Algorithm (RBGSA) in the help of a classifier of Multinomial Naive Bayes. The RBGSA recursively transforms a very raw gene space to an optimized one at each iteration while not degrading the accuracy. We evaluate our model by comparing it with 6 other known methods on 6 different microarray datasets of cancer. Comparison results show that our model gets substantial improvements in accuracy over other methods.