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

arXiv: https://arxiv.org/abs/2110.14166 · HTML: https://mlsanity.com/paper/2110.14166/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2021.

## Built on by (most cited first, 4 of 4)

- [MEL: Efficient Multi-Task Evolutionary Learning for High-Dimensional Feature Selection](https://mlsanity.com/paper/2402.08982/) (2024)
- [SFE: A Simple, Fast and Efficient Feature Selection Algorithm for High-Dimensional Data](https://mlsanity.com/paper/2303.10182/) (2023)
- [Meta-Sel: Efficient Demonstration Selection for In-Context Learning via Supervised Meta-Learning](https://mlsanity.com/paper/2602.12123/) (2026)
- [Geometric Manifold Rectification for Imbalanced Learning](https://mlsanity.com/paper/2602.13045/) (2026)

## Abstract (arXiv)

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.