A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection

TL;DR

This paper introduces SaWDE, a self-adaptive weighted differential evolution algorithm designed to effectively handle large-scale feature selection problems by enhancing diversity, adaptively selecting strategies, and identifying important features.

Contribution

The paper proposes a novel SaWDE algorithm with a multi-population, self-adaptive strategy selection, and weighted feature importance modeling for large-scale feature selection.

Findings

SaWDE outperforms six non-EC and six EC algorithms on twelve datasets.

SaWDE demonstrates robustness and efficiency on high-dimensional data.

The method effectively identifies relevant features in large-scale problems.

Abstract

Recently, many evolutionary computation methods have been developed to solve the feature selection problem. However, the studies focused mainly on small-scale issues, resulting in stagnation issues in local optima and numerical instability when dealing with large-scale feature selection dilemmas. To address these challenges, this paper proposes a novel weighted differential evolution algorithm based on self-adaptive mechanism, named SaWDE, to solve large-scale feature selection. First, a multi-population mechanism is adopted to enhance the diversity of the population. Then, we propose a new self-adaptive mechanism that selects several strategies from a strategy pool to capture the diverse characteristics of the datasets from the historical information. Finally, a weighted model is designed to identify the important features, which enables our model to generate the most suitable feature-selection solution. We demonstrate the effectiveness of our algorithm on twelve large-scale datasets. The performance of SaWDE is superior compared to six non-EC algorithms and six other EC algorithms, on both training and test datasets and on subset size, indicating that our algorithm is a favorable tool to solve the large-scale feature selection problem. Moreover, we have experimented SaWDE with six EC algorithms on twelve higher-dimensional data, which demonstrates that SaWDE is more robust and efficient compared to those state-of-the-art methods. SaWDE source code is available on Github at https://github.com/wangxb96/SaWDE.