Prediction of Gene Selection Features Using Improved Multi-objective Spotted Hyena Optimization Algorithm

Abstract

Microarray data analysis is one of the main research areas in the medical research. The Microarray is a dataset which consists of different gene expressions from which most of the features are redundant genes and reducing the classifier accuracy. Finding a minimal subset of features from large gene expression is a challenging task where removing redundant feature but the important feature will not be missed. Many optimization techniques are introduced by the researchers to find a minimal subset of features but it does not provide a feasible solution. In this paper, the RWeka package, which provides an interface of Weka tool functionality to R is used to order the features using select attribute function in Weka. By using those ordered features, a minimal subset of features is selected using SVM classifier with maximum prediction accuracy in the dataset. Obtained minimal subset of features is given as input to the Multi-Objective Spotted Hyena Optimizer algorithm which is driven by the ensemble of SVM classifier by updating the search agents with objective function with an intension to improve the classification accuracy. The proposed method has experimented with seven publicly available microarray datasets such as CNS, colon, leukemia, lymphoma, lung, MLL, and SRBCT, which shows that the proposed methodology gives the high accuracy than all other existing techniques in terms of feature selection and prediction accuracy.