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ORIGINAL RESEARCH article

Front. Artif. Intell.

Sec. Machine Learning and Artificial Intelligence

Volume 8 - 2025 | doi: 10.3389/frai.2025.1667167

A Dynamic Multitask Evolutionary Algorithm for High-Dimensional Feature Selection Based on Multi-Indicator Task Construction and Elite Competition Learning

Provisionally accepted
Jinxin  TieJinxin Tie1Chunfang  YanChunfang Yan1Maosong  LiMaosong Li2Jianqiang  GongJianqiang Gong1Yujie  WuYujie Wu1Hailin  FangHailin Fang1Meng  LiMeng Li3Weiwei  ZhangWeiwei Zhang3*Jie  LiJie Li1
  • 1China Tobacco Zhejiang Industrial Co Ltd Ningbo Cigarette Factory, Ningbo, China
  • 2China Tobacco Zhejiang Industrial Co Ltd, Hangzhou, China
  • 3Zhengzhou University of Light Industry, Zhengzhou, China

The final, formatted version of the article will be published soon.

High-dimensional data often contain noisy and redundant features, posing challenges for accurate and efficient feature selection. To address this, a dynamic multitask learning framework is proposed, which integrates competitive learning and knowledge transfer within an evolutionary optimization setting. The framework begins by generating two complementary tasks through a multi-criteria strategy that combines multiple feature relevance indicators, ensuring both global comprehensiveness and local focus. These tasks are optimized in parallel using a competitive particle swarm optimization algorithm enhanced with hierarchical elite learning, where each particle learns from both winners and elite individuals to avoid premature convergence. To further improve optimization efficiency and diversity, a probabilistic elite-based knowledge transfer mechanism is introduced, allowing particles to selectively learn from elite solutions across tasks. Experimental results on 13 high-dimensional benchmark datasets demonstrate that the proposed algorithm achieves superior classification accuracy with fewer selected features compared to several state-of-the-art methods. Across 13 benchmarks, the proposed method achieves the highest accuracy on 11 out of 13 datasets and the fewest features on 8 out of 13, with an average accuracy of 87.24% and an average dimensionality reduction of 96.2% (median 200 selected features), clearly validating its effectiveness in balancing exploration, exploitation, and knowledge sharing for robust feature selection.

Keywords: Feature Selection, evolutionary multitask optimization, Elitecompetition, knowledge transfer, High-dimensional data, Tobacco data analytics

Received: 16 Jul 2025; Accepted: 29 Sep 2025.

Copyright: © 2025 Tie, Yan, Li, Gong, Wu, Fang, Li, Zhang and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Weiwei Zhang, anqikeli@126.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.