{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T18:34:35Z","timestamp":1775586875179,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62376089"],"award-info":[{"award-number":["62376089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302153"],"award-info":[{"award-number":["62302153"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302154"],"award-info":[{"award-number":["62302154"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U23A20318"],"award-info":[{"award-number":["U23A20318"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Hubei Province, China","award":["2023BEB024"],"award-info":[{"award-number":["2023BEB024"]}]},{"name":"Young and Middle-aged Scientific and Technological Innovation Team Plan in Higher Education Institutions in Hubei Province, China","award":["T2023007"],"award-info":[{"award-number":["T2023007"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>With the growing complexity of high-dimensional imbalanced datasets in critical fields such as medical diagnosis and bioinformatics, feature selection has become essential to reduce computational costs, alleviate model bias, and improve classification performance. DS-IHBO, a dynamic surrogate-assisted feature selection algorithm integrating relevance-based redundant feature filtering and an improved hybrid breeding algorithm, is presented in this paper. Departing from traditional surrogate-assisted approaches that use static approximations, DS-IHBO employs a dynamic surrogate switching mechanism capable of adapting to diverse data distributions and imbalance ratios through multiple surrogate units built via clustering. It enhances the hybrid breeding algorithm with asymmetric stratified population initialization, adaptive differential operators, and t-distribution mutation strategies to strengthen its global exploration and convergence accuracy. Tests on 12 real-world imbalanced datasets (4\u201398% imbalance) show that DS-IHBO achieves a 3.48% improvement in accuracy, a 4.80% improvement in F1 score, and an 83.85% reduction in computational time compared with leading methods. These results demonstrate its effectiveness for high-dimensional imbalanced feature selection and strong potential for real-world applications.<\/jats:p>","DOI":"10.3390\/sym17101735","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T06:06:32Z","timestamp":1760508392000},"page":"1735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Dynamic Surrogate-Assisted Hybrid Breeding Algorithm for High-Dimensional Imbalanced Feature Selection"],"prefix":"10.3390","volume":"17","author":[{"given":"Yujun","family":"Ma","sequence":"first","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binjing","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1218-0681","authenticated-orcid":false,"given":"Zhiwei","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Computer Science, Hubei University of Technology, Wuhan 430068, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102918","DOI":"10.1109\/ACCESS.2024.3432312","article-title":"Evaluating the computational advantages of the variational quantum circuit model in financial fraud detection","volume":"12","author":"Tudisco","year":"2024","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104809","DOI":"10.1016\/j.chemolab.2023.104809","article-title":"An adaptive loss backward feature elimination method for class-imbalanced and mixed-type data in medical diagnosis","volume":"236","author":"Fu","year":"2023","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1007\/s10462-023-10675-1","article-title":"Gene selection for high dimensional biological datasets using hybrid island binary artificial bee colony with chaos game optimization","volume":"57","author":"Nssibi","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112455","DOI":"10.1016\/j.asoc.2024.112455","article-title":"Anomaly-based network intrusion detection using denoising autoencoder and Wasserstein GAN synthetic attacks","volume":"168","author":"Arafah","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123667","DOI":"10.1016\/j.eswa.2024.123667","article-title":"Analysis and comparison of feature selection methods towards performance and stability","volume":"249","author":"Barbieri","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/j.neunet.2023.10.043","article-title":"Multi-scale feature selection network for lightweight image super-resolution","volume":"169","author":"Li","year":"2024","journal-title":"Neural Netw."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1063","DOI":"10.1007\/s12652-018-1075-x","article-title":"Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection","volume":"15","author":"Sharif","year":"2024","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TEVC.2022.3197427","article-title":"A constrained competitive swarm optimizer with an SVM-based surrogate model for feature selection","volume":"28","author":"Nguyen","year":"2024","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hakami, A. (2024). Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-59958-9"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"106901","DOI":"10.1016\/j.knosys.2021.106901","article-title":"Ensemble learning-based filter-centric hybrid feature selection framework for high-dimensional imbalanced data","volume":"220","author":"Kim","year":"2021","journal-title":"Knowl.-Based Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2213","DOI":"10.1109\/TSMC.2023.3340919","article-title":"Pseudo gradient-adjusted particle swarm optimization for accurate adaptive latent factor analysis","volume":"54","author":"Luo","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6503","DOI":"10.1109\/TCYB.2023.3253701","article-title":"Event-triggered SMC for networked Markov jumping systems with channel fading and applications: Genetic algorithm","volume":"53","author":"Qi","year":"2023","journal-title":"IEEE Trans. Cybern."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6358","DOI":"10.1109\/TCYB.2024.3454346","article-title":"Adaptive ant colony optimization algorithm based on real-time logistics features for instant delivery","volume":"54","author":"Hou","year":"2024","journal-title":"IEEE Trans. Cybern."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"101795","DOI":"10.1016\/j.swevo.2024.101795","article-title":"Elite-driven grey wolf optimization for global optimization and its application to feature selection","volume":"92","author":"Zhang","year":"2025","journal-title":"Swarm Evol. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"3753","DOI":"10.1109\/TFUZZ.2024.3382398","article-title":"A self-learning discrete artificial bee colony algorithm for energy-efficient distributed heterogeneous L-R fuzzy welding shop scheduling problem","volume":"32","author":"Yu","year":"2024","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sadeeq, H.T. (2025). Cauchy Operator Boosted Artificial Rabbits Optimization for Solving Power System Problems. Eng, 6.","DOI":"10.3390\/eng6080174"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"4339","DOI":"10.1007\/s13042-023-01897-4","article-title":"Feature selection using symmetric uncertainty and hybrid optimization for high-dimensional data","volume":"14","author":"Sun","year":"2023","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ye, Z., Ma, L., and Chen, H. (2016, January 23\u201325). A hybrid rice optimization algorithm. Proceedings of the 11th International Conference on Computer Science & Education (ICCSE), Nagoya, Japan.","DOI":"10.1109\/ICCSE.2016.7581575"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.future.2023.09.035","article-title":"An ensemble framework with improved hybrid breeding optimization-based feature selection for intrusion detection","volume":"151","author":"Ye","year":"2024","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111901","DOI":"10.1016\/j.patcog.2025.111901","article-title":"A cooperative hybrid breeding swarm intelligence algorithm for feature selection","volume":"169","author":"Mei","year":"2026","journal-title":"Pattern Recognit."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wei, C., Peng, B., Li, C., Liu, Y., Ye, Z., and Zuo, Z. (2025). A two-stage optimized robust kernel density estimation for Bayesian classification with outliers. Int. J. Mach. Learn. Cybern., 1\u201325.","DOI":"10.1007\/s13042-024-02499-4"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2039","DOI":"10.1080\/00207721.2012.684449","article-title":"An application of a GA with Markov network surrogate to feature selection","volume":"44","author":"Brownlee","year":"2013","journal-title":"Int. J. Syst. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1109\/TEVC.2021.3134804","article-title":"Correlation-guided updating strategy for feature selection in classification with surrogate-assisted particle swarm optimization","volume":"26","author":"Chen","year":"2022","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Squillero, G., and Burelli, P. (2017). Surrogate-model based particle swarm optimisation with local search for feature selection in classification. Applications of Evolutionary Computation, Springer International Publishing.","DOI":"10.1007\/978-3-319-55849-3"},{"key":"ref_25","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":"2022","journal-title":"IEEE Trans. Cybern."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"134651","DOI":"10.1109\/ACCESS.2024.3462636","article-title":"Enhancing DoS detection in WSNs using enhanced ant colony optimization algorithm","volume":"12","author":"Aljughaiman","year":"2024","journal-title":"IEEE Access"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111979","DOI":"10.1016\/j.asoc.2024.111979","article-title":"A memory interaction quadratic interpolation whale optimization algorithm based on reverse information correction for high-dimensional feature selection","volume":"164","author":"Miao","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1896","DOI":"10.1109\/TEVC.2023.3238420","article-title":"SFE: A simple, fast, and efficient feature selection algorithm for high-dimensional data","volume":"27","author":"Ahadzadeh","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pan, J.S., Shi, H.J., Chu, S.C., Hu, P., and Shehadeh, H.A. (2023). Parallel Binary Rafflesia Optimization Algorithm and Its Application in Feature Selection Problem. Symmetry, 15.","DOI":"10.3390\/sym15051073"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1109\/TEVC.2023.3254155","article-title":"An evolutionary multitasking algorithm with multiple filtering for high-dimensional feature selection","volume":"27","author":"Li","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3687485","article-title":"Feature selection as deep sequential generative learning","volume":"18","author":"Ying","year":"2024","journal-title":"ACM Trans. Knowl. Discov. Data"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"110031","DOI":"10.1016\/j.asoc.2023.110031","article-title":"A high-dimensional feature selection method based on modified Gray Wolf optimization","volume":"135","author":"Pan","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"107625","DOI":"10.1016\/j.asoc.2021.107625","article-title":"A multi-surrogate-assisted dual-layer ensemble feature selection algorithm","volume":"110","author":"Jiang","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111380","DOI":"10.1016\/j.knosys.2024.111380","article-title":"Information gain ratio-based subfeature grouping empowers particle swarm optimization for feature selection","volume":"286","author":"Gao","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"103217","DOI":"10.1016\/j.ipm.2022.103217","article-title":"A novel personality detection method based on high-dimensional psycholinguistic features and improved distributed Gray Wolf optimizer for feature selection","volume":"60","author":"Lin","year":"2023","journal-title":"Inf. Process. Manag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Song, X., Jiang, Z., Zhang, Y., Peng, C., and Guo, Y. (2025). A surrogate-assisted multi-phase ensemble feature selection algorithm with particle swarm optimization in imbalanced data. IEEE Trans. Emerg. Top. Comput. Intell., 1\u201316.","DOI":"10.1109\/TETCI.2025.3548786"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1109\/TEVC.2022.3175226","article-title":"Surrogate sample-assisted particle swarm optimization for feature selection on high-dimensional data","volume":"27","author":"Song","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1109\/TEVC.2022.3149601","article-title":"A Surrogate-Assisted Evolutionary Feature Selection Algorithm With Parallel Random Grouping for High-Dimensional Classification","volume":"26","author":"Liu","year":"2022","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5933","DOI":"10.1007\/s40747-024-01465-5","article-title":"Surrogate-assisted evolutionary algorithms for expensive combinatorial optimization: A survey","volume":"10","author":"Liu","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111876","DOI":"10.1016\/j.asoc.2024.111876","article-title":"A multi-strategy surrogate-assisted social learning particle swarm optimization for expensive optimization and applications","volume":"162","author":"Chu","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7305","DOI":"10.1109\/TSMC.2024.3450278","article-title":"A Space Transformation-Based Multiform Approach for Multiobjective Feature Selection in High-Dimensional Classification","volume":"54","author":"Yu","year":"2024","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1007\/s10489-025-06474-6","article-title":"A filter-wrapper model for high-dimensional feature selection based on evolutionary computation","volume":"55","author":"Hu","year":"2025","journal-title":"Appl. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"107865","DOI":"10.1016\/j.engappai.2024.107865","article-title":"A new filter feature selection algorithm for classification task by ensembling Pearson correlation coefficient and mutual information","volume":"131","author":"Gong","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"123014","DOI":"10.1016\/j.eswa.2023.123014","article-title":"An intrusion detection algorithm based on joint symmetric uncertainty and hyperparameter optimized fusion neural network","volume":"244","author":"Wang","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1002\/int.21833","article-title":"Fast-mRMR: Fast minimum redundancy maximum relevance algorithm for high-dimensional big data","volume":"32","author":"Lastra","year":"2017","journal-title":"Int. J. Intell. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"121193","DOI":"10.1016\/j.ins.2024.121193","article-title":"A histogram SMOTE-based sampling algorithm with incremental learning for imbalanced data classification","volume":"686","author":"Liaw","year":"2025","journal-title":"Inf. Sci."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"110111","DOI":"10.1016\/j.knosys.2022.110111","article-title":"A hybrid artificial immune optimization for high-dimensional feature selection","volume":"260","author":"Zhu","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"102205","DOI":"10.1016\/j.jksuci.2024.102205","article-title":"IMOABC: An efficient multi-objective filter\u2013wrapper hybrid approach for high-dimensional feature selection","volume":"36","author":"Li","year":"2024","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"119136","DOI":"10.1016\/j.ins.2023.119136","article-title":"Fast SVM classifier for large-scale classification problems","volume":"642","author":"Wang","year":"2023","journal-title":"Inf. Sci."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1735\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T09:03:21Z","timestamp":1760519001000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/17\/10\/1735"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,14]]},"references-count":49,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["sym17101735"],"URL":"https:\/\/doi.org\/10.3390\/sym17101735","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,14]]}}}