Feature Selection using Multi-Verse Optimization for Brain Tumour Classification

Abstract

Selecting the feature will reduce the dimensionality of the characteristics and the consistency of the reserved features can be maintained in the choir. The selection of features represents a machine-learning optimization challenge, reducing the number of features, eliminating obsolete, noisy, and repetitive data, and achieving suitable acknowledgement. In this paper, Multi-verse Optimization (MVO) is used to select the features from segmented MR image with Random Forest classifier. The steps involved for feature selection- pre-processing, tumor segmentation, feature extraction, feature selection and classification. Two different objective functions are used- image entropy is used as objective function for tumor segmentation and accuracy function is used as objective function for feature classification. Experimental results show that performance of MVO+RF approach gives high accuracy compared to existing techniques like PSO and BAT.