{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T16:00:17Z","timestamp":1767715217260,"version":"3.48.0"},"reference-count":105,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T00:00:00Z","timestamp":1765152000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Neurocomputing"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1016\/j.neucom.2025.132361","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T07:42:26Z","timestamp":1765266146000},"page":"132361","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Training neural network classifiers on multi-class imbalanced data via competitive coevolution"],"prefix":"10.1016","volume":"667","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5623-7491","authenticated-orcid":false,"given":"Marco","family":"Castellani","sequence":"first","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.neucom.2025.132361_bib0005","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1109\/TKDE.2015.2458858","article-title":"To combat multi-class imbalanced problems by means of over-sampling techniques","volume":"28","author":"Abdi","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2025.132361_bib0010","doi-asserted-by":"crossref","first-page":"3559","DOI":"10.1007\/s40747-021-00614-4","article-title":"Combining weighted smote with ensemble learning for the class-imbalanced prediction of small business credit risk","volume":"9","author":"Abedin","year":"2023","journal-title":"Complex Intell. Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0015","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/0306-9877(92)90093-R","article-title":"Mechanisms of adaptive evolution. Darwinism and lamarckism restated","volume":"38","author":"Aboitiz","year":"1992","journal-title":"Med. Hypotheses"},{"key":"10.1016\/j.neucom.2025.132361_bib0020","series-title":"2015 7Th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3k)","first-page":"226","article-title":"Scut: multi-class imbalanced data classification using smote and cluster-based undersampling","author":"Agrawal","year":"2015"},{"key":"10.1016\/j.neucom.2025.132361_bib0025","series-title":"Computational Intelligence, Theory and Applications: International Conference 8th Fuzzy Days in Dortmund, Germany, Sept. 29\u2013Oct. 01, 2004 Proceedings","first-page":"503","article-title":"Co-evolving multilayer perceptrons along training sets","author":"Arenas","year":"2005"},{"key":"10.1016\/j.neucom.2025.132361_bib0030","first-page":"B1","article-title":"Handbook of evolutionary computation","volume":"97","author":"B\u00e4ck","year":"1997","journal-title":"Release"},{"key":"10.1016\/j.neucom.2025.132361_bib0035","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1145\/1007730.1007735","article-title":"A study of the behavior of several methods for balancing machine learning training data","volume":"6","author":"Batista","year":"2004","journal-title":"ACM SIGKDD Explor. Newsl."},{"key":"10.1016\/j.neucom.2025.132361_bib0040","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/A:1018054314350","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"10.1016\/j.neucom.2025.132361_bib0045","series-title":"2023 15th International Conference on Knowledge and Systems Engineering (KSE)","first-page":"1","article-title":"A multi-objective co-operative co-evolutionary method for classification with imbalanced data","author":"Bui","year":"2023"},{"key":"10.1016\/j.neucom.2025.132361_bib0050","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1016\/j.neucom.2012.07.010","article-title":"Evolutionary generation of neural network classifiers \u2014 an empirical comparison","volume":"99","author":"Castellani","year":"2013","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2025.132361_bib0055","doi-asserted-by":"crossref","first-page":"3417","DOI":"10.1007\/s00500-017-2587-6","article-title":"Competitive co-evolution of multi-layer perceptron classifiers","volume":"22","author":"Castellani","year":"2018","journal-title":"Soft Comput."},{"key":"10.1016\/j.neucom.2025.132361_bib0060","unstructured":"M. Castellani, Competitive coevolution for training neural network classifiers on multi-class imbalanced data - experimental results (will be published upon paper acceptance in journal) (2025) data.mendeley.com\/preview\/w8dw9dfmt7?a=10d4d4d1-dbb6-4f85-8513-631a59825e77."},{"key":"10.1016\/j.neucom.2025.132361_bib0065","series-title":"Mendel","first-page":"41","article-title":"An experimental study on competitive coevolution of MLP classifiers","author":"Castellani","year":"2017"},{"key":"10.1016\/j.neucom.2025.132361_bib0070","doi-asserted-by":"crossref","first-page":"732","DOI":"10.1016\/j.engappai.2009.01.013","article-title":"Evolutionary artificial neural network design and training for wood veneer classification","volume":"22","author":"Castellani","year":"2009","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.neucom.2025.132361_bib0075","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/TNNLS.2013.2246188","article-title":"Novel cost-sensitive approach to improve the multilayer perceptron performance on imbalanced data","volume":"24","author":"Castro","year":"2013","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107104","article-title":"Highly imbalanced fault classification of wind turbines using data resampling and hybrid ensemble method approach","volume":"126","author":"Chatterjee","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.neucom.2025.132361_bib0085","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"Smote: synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"10.1016\/j.neucom.2025.132361_bib0090","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10462-024-10759-6","article-title":"A survey on imbalanced learning: latest research, applications and future directions","volume":"57","author":"Chen","year":"2024","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.neucom.2025.132361_bib0095","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Trans. Inf. Theory"},{"key":"10.1016\/j.neucom.2025.132361_bib0100","series-title":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","first-page":"363","article-title":"Using evolutionary sampling to mine imbalanced data","author":"Drown","year":"2007"},{"key":"10.1016\/j.neucom.2025.132361_bib0105","first-page":"33","article-title":"An improved model using oversampling technique and cost-sensitive learning for imbalanced data problem","volume":"2","author":"El-Amir","year":"2024","journal-title":"Inf. Sci. Appl."},{"key":"10.1016\/j.neucom.2025.132361_bib0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2021.114582","article-title":"Conditional Wasserstein Gan-based oversampling of tabular data for imbalanced learning","volume":"174","author":"Engelmann","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2025.132361_bib0115","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.knosys.2013.01.018","article-title":"Analysing the classification of imbalanced data-sets with multiple classes: binarization techniques and ad-hoc approaches","volume":"42","author":"Fern\u00e1ndez","year":"2013","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0120","doi-asserted-by":"crossref","first-page":"1821","DOI":"10.1016\/j.patcog.2011.02.019","article-title":"A dynamic over-sampling procedure based on sensitivity for multi-class problems","volume":"44","author":"Fern\u00e1ndez-Navarro","year":"2011","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.neucom.2025.132361_bib0125","first-page":"35","article-title":"Boosting algorithms: a review of methods, theory, and applications","author":"Ferreira","year":"2012","journal-title":"Ensemble Mach. Learn.: Methods Appl."},{"key":"10.1016\/j.neucom.2025.132361_bib0130","doi-asserted-by":"crossref","unstructured":"D.B. Fogel, Evolutionary algorithms in theory and practice (1997).","DOI":"10.1002\/(SICI)1099-0526(199703\/04)2:4<26::AID-CPLX6>3.3.CO;2-Z"},{"year":"1966","series-title":"Artificial Intelligence Through Simulated Evolution","author":"Fogel","key":"10.1016\/j.neucom.2025.132361_bib0135"},{"key":"10.1016\/j.neucom.2025.132361_bib0140","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1109\/TSMCC.2011.2161285","article-title":"A review on ensembles for the class imbalance problem: bagging-, boosting-, and hybrid-based approaches","volume":"42","author":"Galar","year":"2011","journal-title":"IEEE Trans. Syst. Man Cybern. C Appl. Rev."},{"key":"10.1016\/j.neucom.2025.132361_bib0145","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1016\/j.patcog.2013.05.006","article-title":"Eusboost: enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling","volume":"46","author":"Galar","year":"2013","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.neucom.2025.132361_bib0150","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1162\/evco.2009.17.3.275","article-title":"Evolutionary undersampling for classification with imbalanced datasets: proposals and taxonomy","volume":"17","author":"Garc\u00eda","year":"2009","journal-title":"Evolutionary Computation"},{"key":"10.1016\/j.neucom.2025.132361_bib0155","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1007\/s11518-022-5545-5","article-title":"A hybrid evolutionary under-sampling method for handling the class imbalance problem with overlap in credit classification","volume":"31","author":"Gong","year":"2022","journal-title":"J. Syst. Sci. Syst. Eng."},{"key":"10.1016\/j.neucom.2025.132361_bib0160","article-title":"Generative adversarial nets","volume":"27","author":"Goodfellow","year":"2014","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0165","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1093\/jamia\/ocac093","article-title":"The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression","volume":"29","author":"van den Goorbergh","year":"2022","journal-title":"J. Am. Med. Inform. Assoc."},{"key":"10.1016\/j.neucom.2025.132361_bib0170","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2024.120311","article-title":"Awgan: an adaptive weighting GAN approach for oversampling imbalanced datasets","volume":"663","author":"Guan","year":"2024","journal-title":"Inf. Sci."},{"key":"10.1016\/j.neucom.2025.132361_bib0175","series-title":"International Conference on Intelligent Computing","first-page":"878","article-title":"Borderline-Smote: a new over-sampling method in imbalanced data sets learning","author":"Han","year":"2005"},{"key":"10.1016\/j.neucom.2025.132361_bib0180","first-page":"2471","article-title":"A survey of multi-class imbalanced data classification methods","volume":"44","author":"Han","year":"2023","journal-title":"J. Intell. Fuzzy Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0185","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0274-4","article-title":"Severely imbalanced big data challenges: investigating data sampling approaches","volume":"6","author":"Hasanin","year":"2019","journal-title":"J. Big Data"},{"key":"10.1016\/j.neucom.2025.132361_bib0190","series-title":"2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","first-page":"1322","article-title":"Adasyn: adaptive synthetic sampling approach for imbalanced learning","author":"He","year":"2008"},{"key":"10.1016\/j.neucom.2025.132361_bib0195","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","article-title":"Learning from imbalanced data","volume":"21","author":"He","year":"2009","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2025.132361_bib0200","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/0893-6080(89)90020-8","article-title":"Multilayer feedforward networks are universal approximators","volume":"2","author":"Hornik","year":"1989","journal-title":"Neural networks"},{"key":"10.1016\/j.neucom.2025.132361_bib0205","series-title":"2024 International Conference on Information Networking (ICOIN)","first-page":"708","article-title":"A k-means clustering based under-sampling method for imbalanced dataset classification","author":"Huang","year":"2024"},{"key":"10.1016\/j.neucom.2025.132361_bib0210","doi-asserted-by":"crossref","first-page":"429","DOI":"10.3233\/IDA-2002-6504","article-title":"The class imbalance problem: a systematic study","volume":"6","author":"Japkowicz","year":"2002","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.neucom.2025.132361_bib0215","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1145\/1007730.1007737","article-title":"Class imbalances versus small disjuncts","volume":"6","author":"Jo","year":"2004","journal-title":"ACM Sigkdd Explor. Newsl."},{"key":"10.1016\/j.neucom.2025.132361_bib0220","doi-asserted-by":"crossref","first-page":"3573","DOI":"10.1109\/TNNLS.2017.2732482","article-title":"Cost-sensitive learning of deep feature representations from imbalanced data","volume":"29","author":"Khan","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"author":"Kingma","key":"10.1016\/j.neucom.2025.132361_bib0225"},{"year":"2021","series-title":"Introductory Statistics: A Problem-Solving Approach","author":"Kokoska","key":"10.1016\/j.neucom.2025.132361_bib0230"},{"key":"10.1016\/j.neucom.2025.132361_bib0235","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s42600-023-00307-6","article-title":"Bagged based ensemble model to predict thyroid disorder using linear discriminant analysis with smote","volume":"39","author":"Kour","year":"2023","journal-title":"Res. Biomed. Eng."},{"key":"10.1016\/j.neucom.2025.132361_bib0240","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1007\/s13748-016-0094-0","article-title":"Learning from imbalanced data: open challenges and future directions","volume":"5","author":"Krawczyk","year":"2016","journal-title":"Prog. Artif. Intell."},{"key":"10.1016\/j.neucom.2025.132361_bib0245","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.asoc.2015.08.060","article-title":"Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy","volume":"38","author":"Krawczyk","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2025.132361_bib0250","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/8460934","article-title":"A hybrid approach using oversampling technique and cost-sensitive learning for bankruptcy prediction","volume":"2019","author":"Le","year":"2019","journal-title":"Complexity"},{"key":"10.1016\/j.neucom.2025.132361_bib0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111708","article-title":"Oversampling framework based on sample subspace optimization with accelerated binary particle swarm optimization for imbalanced classification","volume":"162","author":"Li","year":"2024","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2025.132361_bib0260","first-page":"1","article-title":"A binary pso-based ensemble under-sampling model for rebalancing imbalanced training data","author":"Li","year":"2022","journal-title":"J. Supercomput."},{"key":"10.1016\/j.neucom.2025.132361_bib0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2020.105818","article-title":"ACO resampling: enhancing the performance of oversampling methods for class imbalance classification","volume":"196","author":"Li","year":"2020","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0270","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1109\/TNNLS.2012.2228231","article-title":"Dynamic sampling approach to training neural networks for multiclass imbalance classification","volume":"24","author":"Lin","year":"2013","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119703","article-title":"An empirical study of dynamic selection and random under-sampling for the class imbalance problem","volume":"221","author":"Liu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2025.132361_bib0280","first-page":"539","article-title":"Exploratory undersampling for class-imbalance learning","volume":"39","author":"Liu","year":"2008","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"author":"Liu","key":"10.1016\/j.neucom.2025.132361_bib0285"},{"key":"10.1016\/j.neucom.2025.132361_bib0290","first-page":"S1","article-title":"On the generalized distance in statistics","volume":"80","author":"Mahalanobis","year":"2018","journal-title":"Sankhya Indian J. Stat. Ser. A"},{"key":"10.1016\/j.neucom.2025.132361_bib0295","series-title":"Proceedings of Workshop on Learning from Imbalanced Datasets","first-page":"1","article-title":"KNN approach to unbalanced data distributions: a case study involving information extraction","author":"Mani","year":"2003"},{"key":"10.1016\/j.neucom.2025.132361_bib0300","series-title":"Artificial Life X: Proceedings of the Tenth International Conference on the Simulation and Synthesis of Living Systems","first-page":"118","article-title":"The role of space in the success of coevolutionary learning","author":"Mitchell","year":"2006"},{"key":"10.1016\/j.neucom.2025.132361_bib0305","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1177\/1059712311426912","article-title":"Co-evolving predator and prey robots","volume":"20","author":"Nolfi","year":"2012","journal-title":"Adapt. Behav."},{"key":"10.1016\/j.neucom.2025.132361_bib0310","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1162\/artl.1995.2.4.355","article-title":"Coevolutionary computation","volume":"2","author":"Paredis","year":"1995","journal-title":"Artificial Life"},{"key":"10.1016\/j.neucom.2025.132361_bib0315","series-title":"International Conference on Parallel Problem Solving from Nature","first-page":"72","article-title":"Coevolutionary life-time learning","author":"Paredis","year":"1996"},{"key":"10.1016\/j.neucom.2025.132361_bib0320","article-title":"A survey on unbalanced classification: how can evolutionary computation help?","author":"Pei","year":"2023","journal-title":"IEEE Trans. Evol. Comput."},{"key":"10.1016\/j.neucom.2025.132361_bib0325","series-title":"Computational Intelligence","first-page":"67","article-title":"Artificial neural networks","author":"Pham","year":"2007"},{"key":"10.1016\/j.neucom.2025.132361_bib0330","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1007\/978-3-540-92910-9_31","article-title":"Coevolutionary principles","volume":"2","author":"Popovici","year":"2012","journal-title":"Handbook of Nat. Comput."},{"key":"10.1016\/j.neucom.2025.132361_bib0335","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1007\/s10710-024-09481-7","article-title":"Neural network crossover in genetic algorithms using genetic programming","volume":"25","author":"Pretorius","year":"2024","journal-title":"Genet. Program. Evolvable Mach."},{"key":"10.1016\/j.neucom.2025.132361_bib0340","series-title":"2015 IEEE International Conference on Information Reuse and Integration","first-page":"197","article-title":"Using random undersampling to alleviate class imbalance on tweet sentiment data","author":"Prusa","year":"2015"},{"key":"10.1016\/j.neucom.2025.132361_bib0345","series-title":"Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation","first-page":"1073","article-title":"Emergence of competitive and cooperative behavior using coevolution","author":"Rajagopalan","year":"2010"},{"key":"10.1016\/j.neucom.2025.132361_bib0350","series-title":"2007 International Conference on Industrial and Information Systems","first-page":"291","article-title":"Unbalanced decision trees for multi-class classification","author":"Ramanan","year":"2007"},{"key":"10.1016\/j.neucom.2025.132361_bib0355","series-title":"2024 3rd International Conference on Artificial Intelligence for Internet of Things (AIIoT)","first-page":"1","article-title":"A cost-sensitive learning approach with multi-class classification and undersampling techniques for pest identification in the coconut leaf dataset","author":"Ramasamy","year":"2024"},{"key":"10.1016\/j.neucom.2025.132361_bib0360","first-page":"111","article-title":"An approach to the synthesis of life","author":"Ray","year":"1996","journal-title":"The Philos. of Artif. Life"},{"key":"10.1016\/j.neucom.2025.132361_bib0365","series-title":"International Proceedings on Advances in Soft Computing, Intelligent Systems and Applications: ASISA 2016","first-page":"431","article-title":"Handling imbalanced data: a survey","author":"Rout","year":"2018"},{"key":"10.1016\/j.neucom.2025.132361_bib0370","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"nature"},{"key":"10.1016\/j.neucom.2025.132361_bib0375","first-page":"160","article-title":"A review of multi-class classification for imbalanced data","volume":"2","author":"Sahare","year":"2012","journal-title":"Int. J. Adv. Comput. Res."},{"key":"10.1016\/j.neucom.2025.132361_bib0380","doi-asserted-by":"crossref","first-page":"1869","DOI":"10.1162\/089976600300015178","article-title":"Boosting neural networks","volume":"12","author":"Schwenk","year":"2000","journal-title":"Neural Comput."},{"key":"10.1016\/j.neucom.2025.132361_bib0385","article-title":"A hybrid approach to alleviating class imbalance IEEE Transactions on systems","volume":"40","author":"Seiffert","year":"2010","journal-title":"IEEE Trans. Man Cybern. Part A Syst. Hum."},{"key":"10.1016\/j.neucom.2025.132361_bib0390","article-title":"A survey on imbalanced data handling techniques for classification","volume":"9","author":"Sharma","year":"2021","journal-title":"International Journal"},{"key":"10.1016\/j.neucom.2025.132361_bib0395","first-page":"2861","article-title":"Handling class imbalance in online transaction fraud detection","volume":"70","author":"Singla","year":"2021","journal-title":"Comput. Mater. Continua"},{"key":"10.1016\/j.neucom.2025.132361_bib0400","series-title":"2018 International Conference on Current Trends Towards Converging Technologies (ICCTCT)","first-page":"1","article-title":"A review on handling imbalanced data","author":"Spelmen","year":"2018"},{"key":"10.1016\/j.neucom.2025.132361_bib0405","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109637","article-title":"Multi-class imbalanced enterprise credit evaluation based on asymmetric bagging combined with light gradient boosting machine","volume":"130","author":"Sun","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2025.132361_bib0410","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1142\/S0218001409007326","article-title":"Classification of imbalanced data: a review","volume":"23","author":"Sun","year":"2009","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"10.1016\/j.neucom.2025.132361_bib0415","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.neucom.2020.03.064","article-title":"Adaboost-Cnn: an adaptive boosting algorithm for convolutional neural networks to classify multi-class imbalanced datasets using transfer learning","volume":"404","author":"Taherkhani","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2025.132361_bib0420","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s40537-020-00349-y","article-title":"Boosting methods for multi-class imbalanced data classification: an experimental review","volume":"7","author":"Tanha","year":"2020","journal-title":"J. Big Data"},{"key":"10.1016\/j.neucom.2025.132361_bib0425","series-title":"Artificial Neural Nets and Genetic Algorithms: Proceedings of the International Conference in Innsbruck, Austria, 1993","first-page":"658","article-title":"Genetic weight optimization of a feedforward neural network controller","author":"Thierens","year":"1993"},{"key":"10.1016\/j.neucom.2025.132361_bib0430","series-title":"2016 IEEE Congress on Evolutionary Computation (CEC)","first-page":"640","article-title":"Evolutionary undersampling for extremely imbalanced big data classification under Apache Spark","author":"Triguero","year":"2016"},{"key":"10.1016\/j.neucom.2025.132361_bib0435","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1002\/sam.11613","article-title":"Adaptive boosting for ordinal target variables using neural networks","volume":"16","author":"Um","year":"2023","journal-title":"Stat. Anal. Data Min.: ASA Data Sci. J."},{"year":"1947","series-title":"Theory of Games and Economic Behavior, 2nd Rev","author":"Von Neumann","key":"10.1016\/j.neucom.2025.132361_bib0440"},{"key":"10.1016\/j.neucom.2025.132361_bib0445","doi-asserted-by":"crossref","first-page":"67","DOI":"10.3390\/diagnostics13010067","article-title":"A comparison of techniques for class imbalance in deep learning classification of breast cancer","volume":"13","author":"Walsh","year":"2022","journal-title":"Diagnostics"},{"key":"10.1016\/j.neucom.2025.132361_bib0450","series-title":"The 2012 International Joint Conference on Neural Networks (IJCNN)","first-page":"1","article-title":"Applying adaptive over-sampling technique based on data density and cost-sensitive SVM to imbalanced learning","author":"Wang","year":"2012"},{"key":"10.1016\/j.neucom.2025.132361_bib0455","series-title":"Proceedings of the Third International Conference on Genetic Algorithms","first-page":"116","article-title":"The genitor algorithm and selection pressure: why rank-based allocation of reproductive trials is best","author":"Whitley","year":"1989"},{"key":"10.1016\/j.neucom.2025.132361_bib0460","doi-asserted-by":"crossref","first-page":"25452","DOI":"10.48084\/etasr.11925","article-title":"A modified smote with noise filtering and Manhattan distance metric approach to address imbalanced health datasets","volume":"15","author":"Widiyaningtyas","year":"2025","journal-title":"Eng. Technol. Appl. Sci. Res."},{"key":"10.1016\/j.neucom.2025.132361_bib0465","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1016\/j.procs.2024.03.042","article-title":"Machine learning-based intrusion detection on multi-class imbalanced dataset using smote","volume":"234","author":"Widodo","year":"2024","journal-title":"Proc. Comput. Sci."},{"key":"10.1016\/j.neucom.2025.132361_bib0470","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","article-title":"Asymptotic properties of nearest neighbor rules using edited data","author":"Wilson","year":"1972","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.neucom.2025.132361_bib0475","first-page":"6376","article-title":"Spatial distribution-based imbalanced undersampling","volume":"35","author":"Yan","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2025.132361_bib0480","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.3233\/IDA-220383","article-title":"Oversampling method based on GAN for tabular binary classification problems","volume":"27","author":"Yang","year":"2023","journal-title":"Intell. Data Anal."},{"key":"10.1016\/j.neucom.2025.132361_bib0485","doi-asserted-by":"crossref","first-page":"2464","DOI":"10.1109\/TSMC.2021.3051138","article-title":"Progressive hybrid classifier ensemble for imbalanced data","volume":"52","author":"Yang","year":"2021","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0490","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1002\/int.4550080406","article-title":"A review of evolutionary artificial neural networks","volume":"8","author":"Yao","year":"1993","journal-title":"Int. J. Intell. Syst."},{"key":"10.1016\/j.neucom.2025.132361_bib0495","doi-asserted-by":"crossref","first-page":"5718","DOI":"10.1016\/j.eswa.2008.06.108","article-title":"Cluster-based under-sampling approaches for imbalanced data distributions","volume":"36","author":"Yen","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.neucom.2025.132361_bib0500","series-title":"IEEE Conference Anthology","first-page":"1","article-title":"Improved smotebagging and its application in imbalanced data classification","author":"Yongqing","year":"2013"},{"key":"10.1016\/j.neucom.2025.132361_bib0505","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.neucom.2012.08.018","article-title":"Acosampling: an ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data","volume":"101","author":"Yu","year":"2013","journal-title":"Neurocomputing"},{"key":"10.1016\/j.neucom.2025.132361_bib0510","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110662","article-title":"A competitive learning scheme for deep neural network pattern classifier training","volume":"146","author":"Zheng","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.neucom.2025.132361_bib0515","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/TKDE.2006.17","article-title":"Training cost-sensitive neural networks with methods addressing the class imbalance problem","volume":"18","author":"Zhou","year":"2005","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.neucom.2025.132361_bib0520","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1111\/j.1467-8640.2010.00358.x","article-title":"On multi-class cost-sensitive learning","volume":"26","author":"Zhou","year":"2010","journal-title":"Comput. Intell."},{"key":"10.1016\/j.neucom.2025.132361_bib0525","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.patcog.2017.07.024","article-title":"Synthetic minority oversampling technique for multiclass imbalance problems","volume":"72","author":"Zhu","year":"2017","journal-title":"Pattern Recognit."}],"container-title":["Neurocomputing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231225030334?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0925231225030334?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,6]],"date-time":"2026-01-06T15:56:39Z","timestamp":1767714999000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925231225030334"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":105,"alternative-id":["S0925231225030334"],"URL":"https:\/\/doi.org\/10.1016\/j.neucom.2025.132361","relation":{},"ISSN":["0925-2312"],"issn-type":[{"type":"print","value":"0925-2312"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Training neural network classifiers on multi-class imbalanced data via competitive coevolution","name":"articletitle","label":"Article Title"},{"value":"Neurocomputing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.neucom.2025.132361","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"132361"}}