A Variable Granularity Search-Based Multiobjective Feature Selection Algorithm for High-Dimensional Data Classification

Abstract

Evolutionary algorithms (EAs) have shown their competitiveness in solving the problem of feature selection (FS). However, in most of the existing EA-based FS methods, one bit in the individual only represents one feature, which means with the number of features increasing, the search space of these methods increases exponentially and makes them not suitable for the data classification with high dimensions. To tackle the issue, in this article, a variable granularity search-based multiobjective EA, termed as VGS-MOEA, is proposed for high-dimensional FS, where one bit in the individual representation denotes a group of features and results in the search space reducing greatly. To be specific, at the beginning, the search granularity of VGS-MOEA is coarse (a bit denotes a great number of features), which helps the proposed algorithm detect the potentially good feature subsets quickly. As the evolution continues, the search granularity is refined gradually, where a bit denotes a smaller number of features until it only represents one feature. With this decomposition of granularity, a more refined search is performed and leads to the VGS-MOEA obtaining feature subsets with higher quality. Experimental results on 12 high-dimensional data sets with different characteristics have shown that in comparison with the state of the arts, the proposed VGS-MOEA has demonstrated its superiority in terms of the classification accuracy, the number of selected features, and the running time.