WOA-SVM: Whale Optimization Algorithm and Support Vector Machine for Hyperspectral Band Selection and 2D Images Feature Selection

Abstract

This paper proposes a new optimization based framework for feature selection and parameters determination of support vector machine, called WOA-SVM and it is applied on band selection in hyperspectral images and feature selection in 2D images. The proposed approach WOA-SVM is based on Whale Optimization Algorithm (WOA), which is a meta-heuristic inspired from the social behaviors of humpback whale and never been benchmarked in the context of feature selection nor parameters determination. A new fitness function is designed. WOA-SVM is tested with three hyperspectral images widely used for band selection and classification. Note that one of the problems in hyperspectral image classification research is the identification of informative bands (band selection). In addition, we demonstrate the efficiency of the proposed approach on Mammographic Image dataset (MIAS). The experimental results prove that the proposed approach is high performance and very competitive approach. The WOA-SVM approach is useful for parameter determination and feature/band selection in SVM.