{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:56:36Z","timestamp":1776182196687,"version":"3.50.1"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T00:00:00Z","timestamp":1760918400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>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 eight 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.<\/jats:p>","DOI":"10.3389\/frai.2025.1667167","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T05:23:15Z","timestamp":1760937795000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A dynamic multitask evolutionary algorithm for high-dimensional feature selection based on multi-indicator task construction and elite competition learning"],"prefix":"10.3389","volume":"8","author":[{"given":"Jinxin","family":"Tie","sequence":"first","affiliation":[]},{"given":"Chunfang","family":"Yan","sequence":"additional","affiliation":[]},{"given":"Maosong","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jianqiang","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Yujie","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Hailin","family":"Fang","sequence":"additional","affiliation":[]},{"given":"Meng","family":"Li","sequence":"additional","affiliation":[]},{"given":"Weiwei","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,10,20]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"9191","DOI":"10.1007\/s13369-019-04064-6","article-title":"Hybrid filter\u2013wrapper feature selection method for sentiment classification","volume":"44","author":"Ansari","year":"2019","journal-title":"Arab. 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Comput."},{"key":"ref16","doi-asserted-by":"publisher","first-page":"290","DOI":"10.17762\/ijritcc.v10i1s.5851","article-title":"A novel hybrid spotted hyena-swarm optimization (HS-FFO) framework for effective feature selection in iot based cloud security data","volume":"10","author":"Lohitha","year":"2022","journal-title":"Int. J. Recent Innov. Trends Comput. Commun."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"25731","DOI":"10.1038\/s41598-024-77240-w","article-title":"Sentiment classification for insider threat identification using metaheuristic optimized machine learning classifiers","author":"Mladenovic","year":"2024","journal-title":"Sci. Rep."},{"key":"ref18","doi-asserted-by":"publisher","first-page":"719","DOI":"10.1007\/s12652-019-01330-1","article-title":"Feature selection method based on hybrid data transformation and binary binomial cuckoo search","volume":"11","author":"Pandey","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref19","first-page":"769","article-title":"Fitness functions in genetic programming for classification with unbalanced data","volume-title":"Australasian Joint Conference on Artificial Intelligence","author":"Patterson","year":"2007"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1016\/j.neucom.2014.12.098","article-title":"Kernel methods for heterogeneous feature selection","volume":"169","author":"Paul","year":"2015","journal-title":"Neurocomputing"},{"key":"ref21","doi-asserted-by":"publisher","first-page":"1782","DOI":"10.3390\/sym12111782","article-title":"An asymmetric chaotic competitive swarm optimization algorithm for feature selection in high-dimensional data","volume":"12","author":"Pichai","year":"2020","journal-title":"Symmetry"},{"key":"ref22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/0952813X.2023.2183267","article-title":"A review of feature selection methods based on meta-heuristic algorithms","volume":"37","author":"Sadeghian","year":"2025","journal-title":"J. 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Sci."},{"key":"ref24","first-page":"1","volume-title":"Fast Correlation Based Filter (FCBF) with a different search strategy","author":"Senliol","year":"2008"},{"key":"ref25","doi-asserted-by":"publisher","first-page":"127111","DOI":"10.1016\/j.neucom.2023.127111","article-title":"Filter unsupervised spectral feature selection method for mixed data based on a new feature correlation measure","volume":"571","author":"Solorio","year":"2024","journal-title":"Neurocomputing"},{"key":"ref26","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1016\/j.patrec.2020.07.039","article-title":"A supervised filter feature selection method for mixed data based on spectral feature selection and information-theory redundancy analysis","volume":"138","author":"Solorio","year":"2020","journal-title":"Pattern Recogn. Lett."},{"key":"ref27","doi-asserted-by":"crossref","first-page":"9573","DOI":"10.1109\/TCYB.2021.3061152","article-title":"A fast hybrid feature selection based on correlation-guided clustering and particle swarm optimization for high-dimensional data","volume":"52","author":"Song","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref28","first-page":"439","article-title":"Feature clustering for PSO-based feature construction on high-dimensional data","volume":"18","author":"Swesi","year":"2019","journal-title":"J. Inf. Commun. Technol."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1016\/j.patrec.2006.10.008","article-title":"Feature selection algorithm for mixed data with both nominal and continuous features","volume":"28","author":"Tang","year":"2007","journal-title":"Pattern Recogn. Lett."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"3696","DOI":"10.1109\/TCYB.2019.2906383","article-title":"Efficient large-scale multiobjective optimization based on a competitive swarm optimizer","volume":"50","author":"Tian","year":"2019","journal-title":"IEEE Trans Cybern"},{"key":"ref31","doi-asserted-by":"publisher","first-page":"31","DOI":"10.3390\/computation7020031","article-title":"Binary competitive swarm optimizer approaches for feature selection","volume":"7","author":"Too","year":"2019","journal-title":"Computation"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1109\/TEVC.2018.2869405","article-title":"Variable-length particle swarm optimization for feature selection on high-dimensional classification","volume":"23","author":"Tran","year":"2018","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref33","first-page":"481","volume-title":"Adaptive multi-subswarm optimisation for feature selection on high-dimensional classification","author":"Tran","year":"2019"},{"key":"ref34","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/s00521-013-1368-0","article-title":"A review of feature selection methods based on mutual information","volume":"24","author":"Vergara","year":"2014","journal-title":"Neural Comput. 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