A differential evolution based feature combination selection algorithm for high-dimensional data

Feature combination selection is used in object classification to select complementary features that can produce a powerful combination. One active area of selecting feature combinations is genome-wide association studies (GWAS). However, selecting feature combinations from high-dimensional GWAS data faces a serious issue of high computational complexity. In this paper, a fast evolutionary optimization method named search history-guided differential evolution (HGDE) is proposed to deal with the problem. This method applies the search history memorized in a binary space partitioning tree to enhance its power for selecting feature combinations. We perform a comparative study on the proposed HGDE algorithm and other state-of-the-art algorithms using synthetic datasets, and later employ the HGDE algorithm in experiments on a real age-related macular degeneration dataset. The experimental results show that this proposed algorithm has superior performance in the selection of feature combinations. Moreover, the results provide a reference for studying the functional mechanisms of age-related macular degeneration. (C) 2020 Elsevier Inc. All rights reserved.

Automated summary

The paper introduces a fast evolutionary optimization method called search history-guided differential evolution for selecting feature combinations from high-dimensional data. Comparative studies show that the proposed algorithm has superior performance in selecting feature combinations, providing a reference for studying the functional mechanisms of related diseases.