Tri-staged feature selection in multi-class heterogeneous datasets using memetic algorithm and cuckoo search optimization

Abstract

Classification algorithms and their preprocessing operations usually performs on feature selection on homogeneous or heterogeneous attributes, binary or multi-class labels separately. Only very few methods attempt to perform feature selection on datasets with heterogeneous multi-class attributes. In order to bridge this gap with better classification performance, the paper proposes a Tri-staged Feature Selection (TFS) methodology which performs (i) Feature selection using Kruskal Wallis test (ii) Refinement of feature selection using a new Memetic Algorithm with local beam search and genetic algorithm operations and (iii) Further refinement of feature selection using Cuckoo Search algorithm. Proper tradeoff between both exploration and exploitation is maintained in the proposed method. The experimental results on 12 datasets show that the proposed method is better than that of state-of-the-art methods used for feature selection in terms of multi-class accuracy, hamming loss, ranking loss, normalized coverage and convergence rate for multi-class heterogeneous datasets.