{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:28:15Z","timestamp":1758274095860,"version":"3.38.0"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T00:00:00Z","timestamp":1739923200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"National Key Research and Development Program of China","award":["No.2021YFB3101500"],"award-info":[{"award-number":["No.2021YFB3101500"]}]},{"name":"Chongqing Municipal Training Program of Innovation and Entrepreneurship for Undergraduate","award":["No.S20241063267"],"award-info":[{"award-number":["No.S20241063267"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Class imbalance is a prevalent issue in practical applications, which poses significant challenges for classifiers. The large margin distribution machine (LDM) introduces the margin distribution of samples to replace the traditional minimum margin, resulting in extensively enhanced classification performance. However, the hyperplane of LDM tends to be skewed toward the minority class, due to the optimization property for margin means. Moreover, the absence of non-deterministic options and measurement of the confidence level of samples further restricts the capability to manage uncertainty in imbalanced classification tasks. To solve these problems, we propose a novel three-way distance-based fuzzy large margin distribution machine (3W-DBFLDM). Specifically, we introduce a distance-based factor to mitigate the impact of sample size imbalance on classification results by increasing the distance weights of the minority class. Additionally, three-way decision model is introduced to deal with uncertainty, and the model\u2019s robustness is further enhanced by utilizing the fuzzy membership degree that reflects the importance level of each input point. Comparative experiments conducted on UCI datasets demonstrate that the 3W-DBFLDM model surpasses other models in classification accuracy, stability, and robustness. Furthermore, the cost comparison experiment validate that the 3W-DBFLDM model reduces the overall decision cost.<\/jats:p>","DOI":"10.1007\/s40747-025-01797-w","type":"journal-article","created":{"date-parts":[[2025,2,19]],"date-time":"2025-02-19T14:51:04Z","timestamp":1739976664000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A novel three-way distance-based fuzzy large margin distribution machine for imbalance classification"],"prefix":"10.1007","volume":"11","author":[{"given":"Li","family":"Liu","sequence":"first","affiliation":[]},{"given":"Jinrui","family":"Guo","sequence":"additional","affiliation":[]},{"given":"Ziqi","family":"Yin","sequence":"additional","affiliation":[]},{"given":"Rui","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Guojun","family":"Huang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,19]]},"reference":[{"issue":"9","key":"1797_CR1","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA (2009) Learning from imbalanced data. IEEE Trans Knowl Data Eng 21(9):1263\u20131284","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1797_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123783","volume":"251","author":"C Cong","year":"2024","unstructured":"Cong C, Liu S, Rana P, Pagnucco M, Di Ieva A, Berkovsky S, Song Y (2024) Adaptive unified contrastive learning with graph-based feature aggregator for imbalanced medical image classification. Expert Syst Appl 251:123783","journal-title":"Expert Syst Appl"},{"key":"1797_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123745","volume":"249","author":"Yu Lean","year":"2024","unstructured":"Lean Yu, Li M, Liu X (2024) A two-stage case-based reasoning driven classification paradigm for financial distress prediction with missing and imbalanced data. Expert Syst Appl 249:123745","journal-title":"Expert Syst Appl"},{"key":"1797_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120420","volume":"229","author":"H Ren","year":"2023","unstructured":"Ren H, Tang Y, Dong W, Ren S, Jiang L (2023) Duen: dynamic ensemble handling class imbalance in network intrusion detection. Expert Syst Appl 229:120420","journal-title":"Expert Syst Appl"},{"issue":"3","key":"1797_CR5","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.ins.2009.09.021","volume":"180","author":"Y Yao","year":"2010","unstructured":"Yao Y (2010) Three-way decisions with probabilistic rough sets. Inf Sci 180(3):341\u2013353","journal-title":"Inf Sci"},{"key":"1797_CR6","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.knosys.2015.07.036","volume":"91","author":"D Liu","year":"2016","unstructured":"Liu D, Liang D, Wang C (2016) A novel three-way decision model based on incomplete information system. Knowl Based Syst 91:32\u201345","journal-title":"Knowl Based Syst"},{"key":"1797_CR7","doi-asserted-by":"crossref","unstructured":"Yao Y (2009) Three-way decision: an interpretation of rules in rough set theory. In: rough sets and knowledge technology: 4th international conference, pp 642\u2013649","DOI":"10.1007\/978-3-642-02962-2_81"},{"issue":"6","key":"1797_CR8","doi-asserted-by":"publisher","first-page":"1080","DOI":"10.1016\/j.ins.2010.11.019","volume":"181","author":"Y Yao","year":"2011","unstructured":"Yao Y (2011) The superiority of three-way decisions in probabilistic rough set models. Inf Sci 181(6):1080\u20131096","journal-title":"Inf Sci"},{"key":"1797_CR9","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1007\/BF01001956","volume":"11","author":"Z Pawlak","year":"1982","unstructured":"Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341\u2013356","journal-title":"Int J Comput Inf Sci"},{"issue":"3","key":"1797_CR10","doi-asserted-by":"publisher","first-page":"982","DOI":"10.1109\/TFUZZ.2022.3193453","volume":"31","author":"J Deng","year":"2023","unstructured":"Deng J, Zhan J, Herrera-Viedma E, Herrera F (2023) Regret theory-based three-way decision method on incomplete multiscale decision information systems with interval fuzzy numbers. IEEE Trans Fuzzy Syst 31(3):982\u2013996","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1797_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2024.102356","volume":"108","author":"Z Qi","year":"2024","unstructured":"Qi Z, Li H, Liu F, Chen T, Dai J (2024) Fusion decision strategies for multiple criterion preferences based on three-way decision. Inf Fusion 108:102356","journal-title":"Inf Fusion"},{"key":"1797_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122343","volume":"239","author":"J Qian","year":"2024","unstructured":"Qian J, Jiang H, Ying Yu, Wang H, Miao D (2024) Multi-level personalized k-anonymity privacy-preserving model based on sequential three-way decisions. Expert Syst Appl 239:122343","journal-title":"Expert Syst Appl"},{"key":"1797_CR13","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.ins.2021.01.028","volume":"559","author":"W Wang","year":"2021","unstructured":"Wang W, Zhan J, Zhang C (2021) Three-way decisions based multi-attribute decision making with probabilistic dominance relations. Inf Sci 559:75\u201396","journal-title":"Inf Sci"},{"key":"1797_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102008","volume":"101","author":"J Ye","year":"2024","unstructured":"Ye J, Sun B, Bai J, Bao Q, Chu X, Bao K (2024) A preference-approval structure-based non-additive three-way group consensus decision-making approach for medical diagnosis. Inf Fusion 101:102008","journal-title":"Inf Fusion"},{"key":"1797_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121249","volume":"237","author":"W Lei","year":"2024","unstructured":"Lei W, Ma W, Li X, Sun B (2024) Three-way group decision based on regret theory under dual hesitant fuzzy environment: an application in water supply alternatives selection. Expert Syst Appl 237:121249","journal-title":"Expert Syst Appl"},{"key":"1797_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119861","volume":"654","author":"H Wang","year":"2024","unstructured":"Wang H, Yanbing J, Dong P, Wang A, Cabrerizo FJ (2024) Preference-based regret three-way decision method on multiple decision information systems with linguistic z-numbers. Inf Sci 654:119861","journal-title":"Inf Sci"},{"key":"1797_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.122727","volume":"242","author":"H Qin","year":"2024","unstructured":"Qin H, Peng Q, Ma X (2024) A novel interval-valued fermatean fuzzy three-way decision making method with probability dominance relations. Expert Syst Appl 242:122727","journal-title":"Expert Syst Appl"},{"key":"1797_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.111590","volume":"158","author":"X Yi","year":"2024","unstructured":"Yi X, Niu G (2024) Research on multi-view clustering algorithm based on sequential three-way decision. Appl Soft Comput 158:111590","journal-title":"Appl Soft Comput"},{"key":"1797_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.120041","volume":"658","author":"P Liang","year":"2024","unstructured":"Liang P, Lei D, Gao X, Junhua H, Chin KS (2024) A sequential three-way decision model for classification with multilevel information gain and regret value optimization. Inf Sci 658:120041","journal-title":"Inf Sci"},{"issue":"3","key":"1797_CR20","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1007\/BF00994018","volume":"20","author":"C Cortes","year":"1995","unstructured":"Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273\u2013297","journal-title":"Mach Learn"},{"key":"1797_CR21","doi-asserted-by":"crossref","unstructured":"Reyzin L, Schapire RE (2006) How boosting the margin can also boost classifier complexity. In: Proceedings of the 23rd international conference on machine learning, pp 753\u2013760","DOI":"10.1145\/1143844.1143939"},{"key":"1797_CR22","unstructured":"Wang L, Sugiyama M, Yang C, Zhou Z, Feng J (2008) On the margin explanation of boosting algorithms. In: COLT, pp 479\u2013490"},{"key":"1797_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.artint.2013.07.002","volume":"203","author":"W Gao","year":"2013","unstructured":"Gao W, Zhou Z (2013) On the doubt about margin explanation of boosting. Artif Intell 203:1\u201318","journal-title":"Artif Intell"},{"key":"1797_CR24","doi-asserted-by":"crossref","unstructured":"Zhang T, Zhou Z (20214) Large margin distribution machine. In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 313\u2013322","DOI":"10.1145\/2623330.2623710"},{"key":"1797_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2020.107374","volume":"106","author":"L Liu","year":"2020","unstructured":"Liu L, Chu M, Gong R, Peng Y (2020) Nonparallel support vector machine with large margin distribution for pattern classification. Pattern Recogn 106:107374","journal-title":"Pattern Recogn"},{"key":"1797_CR26","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.knosys.2018.04.017","volume":"152","author":"Y Li","year":"2018","unstructured":"Li Y, Wang Y, Bi C, Jiang X (2018) Revisiting transductive support vector machines with margin distribution embedding. Knowl Based Syst 152:200\u2013214","journal-title":"Knowl Based Syst"},{"key":"1797_CR27","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.patrec.2019.05.005","volume":"125","author":"X Zhang","year":"2019","unstructured":"Zhang X, Wang D, Zhou Y, Chen H, Cheng F, Liu M (2019) Kernel modified optimal margin distribution machine for imbalanced data classification. Pattern Recogn Lett 125:325\u2013332","journal-title":"Pattern Recogn Lett"},{"key":"1797_CR28","doi-asserted-by":"publisher","first-page":"7058","DOI":"10.1007\/s10489-020-02166-5","volume":"51","author":"U Gupta","year":"2021","unstructured":"Gupta U, Gupta D (2021) Least squares large margin distribution machine for regression. Appl Intell 51:7058\u20137093","journal-title":"Appl Intell"},{"key":"1797_CR29","doi-asserted-by":"crossref","unstructured":"Zhang T, Zhou Z (2018) Optimal margin distribution clustering. In: Proceedings of the AAAI conference on artificial intelligence, pp 4474\u20134481","DOI":"10.1609\/aaai.v32i1.11737"},{"key":"1797_CR30","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.patrec.2017.09.005","volume":"98","author":"S Abe","year":"2017","unstructured":"Abe S (2017) Unconstrained large margin distribution machines. Pattern Recogn Lett 98:96\u2013102","journal-title":"Pattern Recogn Lett"},{"key":"1797_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2020.103635","volume":"91","author":"W Zhu","year":"2020","unstructured":"Zhu W, Song Y, Xiao Y (2020) Support vector machine classifier with huberized pinball loss. Eng Appl Artif Intell 91:103635","journal-title":"Eng Appl Artif Intell"},{"issue":"2","key":"1797_CR32","doi-asserted-by":"publisher","first-page":"464","DOI":"10.1109\/72.991432","volume":"13","author":"C-F Lin","year":"2002","unstructured":"Lin C-F, Wang S-D (2002) Fuzzy support vector machines. IEEE Trans Neural Netw 13(2):464\u2013471","journal-title":"IEEE Trans Neural Netw"},{"key":"1797_CR33","doi-asserted-by":"crossref","unstructured":"Dong D, Feng M, Kurths J, Zhang L (2024) Fuzzy large margin distribution machine for classification. Int J Mach Learn Cybern 15(5):1891\u20131905","DOI":"10.1007\/s13042-023-02004-3"},{"issue":"11","key":"1797_CR34","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1109\/TFUZZ.2019.2893863","volume":"27","author":"S Rezvani","year":"2019","unstructured":"Rezvani S, Wang X, Pourpanah F (2019) Intuitionistic fuzzy twin support vector machines. IEEE Trans Fuzzy Syst 27(11):2140\u20132151","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1797_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.108231","volume":"115","author":"Z Liang","year":"2022","unstructured":"Liang Z, Zhang L (2022) Intuitionistic fuzzy twin support vector machines with the insensitive pinball loss. Appl Soft Comput 115:108231","journal-title":"Appl Soft Comput"},{"key":"1797_CR36","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.patrec.2016.06.009","volume":"80","author":"F Cheng","year":"2016","unstructured":"Cheng F, Zhang J, Wen C (2016) Cost-sensitive large margin distribution machine for classification of imbalanced data. Pattern Recogn Lett 80:107\u2013112","journal-title":"Pattern Recogn Lett"},{"issue":"5","key":"1797_CR37","first-page":"96","volume":"26","author":"A Atla","year":"2011","unstructured":"Atla A, Tada R, Sheng V, Singireddy N (2011) Sensitivity of different machine learning algorithms to noise. J Comput Sci Coll 26(5):96\u2013103","journal-title":"J Comput Sci Coll"},{"issue":"4","key":"1797_CR38","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1109\/5254.708428","volume":"13","author":"MA Hearst","year":"1998","unstructured":"Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intell Syst Appl 13(4):18\u201328","journal-title":"IEEE Intell Syst Appl"},{"key":"1797_CR39","unstructured":"Smola A (2002) Support vector machines, regularization, optimization, and beyond. Learning with kernels"},{"key":"1797_CR40","unstructured":"Asuncion A, Newman D (2007) Uci machine learning repository. https:\/\/ergodicity.net\/2013\/07\/"},{"key":"1797_CR41","doi-asserted-by":"crossref","unstructured":"Zhang L, Jin Q, Fan S, Liu D (2023) A novel dual-center-based intuitionistic fuzzy twin bounded large margin distribution machines. IEEE Trans Fuzzy Syst 31(9):3121\u20133134. https:\/\/ieeexplore.ieee.org\/abstract\/document\/10045047","DOI":"10.1109\/TFUZZ.2023.3245215"},{"key":"1797_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.109192","volume":"136","author":"X Xie","year":"2023","unstructured":"Xie X, Sun F, Qian J, Guo L, Zhang R, Ye X, Wang Z (2023) Laplacian lp norm least squares twin support vector machine. Pattern Recogn 136:109192","journal-title":"Pattern Recogn"},{"key":"1797_CR43","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.neunet.2022.10.003","volume":"157","author":"MA Hossein Moosaei","year":"2023","unstructured":"Hossein Moosaei MA, Ganaie MH, Tanveer M (2023) Inverse free reduced universum twin support vector machine for imbalanced data classification. Neural Netw 157:125\u2013135","journal-title":"Neural Netw"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01797-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-01797-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-01797-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T12:07:53Z","timestamp":1741090073000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-01797-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,19]]},"references-count":43,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["1797"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-01797-w","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"type":"print","value":"2199-4536"},{"type":"electronic","value":"2198-6053"}],"subject":[],"published":{"date-parts":[[2025,2,19]]},"assertion":[{"value":"18 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"176"}}