{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T16:18:50Z","timestamp":1792858730515,"version":"4.5.0"},"reference-count":58,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100003482","name":"Department of Education of Hebei Province","doi-asserted-by":"publisher","award":["BJ2025217"],"award-info":[{"award-number":["BJ2025217"]}],"id":[{"id":"10.13039\/501100003482","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.eswa.2026.131845","type":"journal-article","created":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T16:18:29Z","timestamp":1772036309000},"page":"131845","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":5,"special_numbering":"C","title":["KCPIA: Kolmogorov complexity-based positive instance augmentation for class-imbalance problem"],"prefix":"10.1016","volume":"316","author":[{"given":"Qi","family":"Dai","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kexin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tony","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3180-7347","authenticated-orcid":false,"given":"Weiping","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4326-7023","authenticated-orcid":false,"given":"Lifang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.eswa.2026.131845_b0005","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 Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"10.1016\/j.eswa.2026.131845_b0010","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1186\/s40537-024-00982-x","article-title":"CTGAN-ENN: A tabular GAN-based hybrid sampling method for imbalanced and overlapped data in customer churn prediction","volume":"11","author":"Adiputra","year":"2024","journal-title":"Journal of Big Data"},{"key":"10.1016\/j.eswa.2026.131845_b0020","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1023\/A:1018054314350","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Machine Learning"},{"key":"10.1016\/j.eswa.2026.131845_b0025","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"10.1016\/j.eswa.2026.131845_b0030","doi-asserted-by":"crossref","unstructured":"Bunkhumpornpat, C., Sinapiromsaran, K., & Lursinsap, C. (2009). Safe-level-smote: Safe-level-synthetic minority over-sampling technique for handling the class imbalanced problem. In Advances in Knowledge Discovery and Data Mining: 13th Pacific-Asia Conference, PAKDD 2009 Bangkok, Thailand, April 27-30, 2009 Proceedings 13 (pp. 475-482). Springer Berlin Heidelberg. DOI: 10.1007\/978-3-642-01307-2_43.","DOI":"10.1007\/978-3-642-01307-2_43"},{"key":"10.1016\/j.eswa.2026.131845_b0035","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":"Journal of Artificial Intelligence Research"},{"issue":"1","key":"10.1016\/j.eswa.2026.131845_b0040","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1007730.1007733","article-title":"Special issue on learning from imbalanced data sets","volume":"6","author":"Chawla","year":"2004","journal-title":"ACM SIGKDD Explorations Newsletter"},{"key":"10.1016\/j.eswa.2026.131845_b0045","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.neucom.2022.09.139","article-title":"Fuzzy support vector machine with graph for classifying imbalanced datasets","volume":"514","author":"Chen","year":"2022","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.eswa.2026.131845_b0050","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"Cohen","year":"1960","journal-title":"Educational and Psychological Measurement"},{"key":"10.1016\/j.eswa.2026.131845_b0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.109083","article-title":"Multi-granularity relabeled under-sampling algorithm for imbalanced data","volume":"124","author":"Dai","year":"2022","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2026.131845_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119735","article-title":"Class-overlap undersampling based on Schur decomposition for Class-imbalance problems","volume":"221","author":"Dai","year":"2023","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131845_b0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109902","article-title":"Class-imbalanced positive instances augmentation via three-line hybrid","volume":"257","author":"Dai","year":"2022","journal-title":"Knowledge-Based Systems"},{"issue":"2","key":"10.1016\/j.eswa.2026.131845_b0070","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.1007\/s00521-022-07828-8","article-title":"Distance-based arranging oversampling technique for imbalanced data","volume":"35","author":"Dai","year":"2023","journal-title":"Neural Computing and Applications"},{"key":"10.1016\/j.eswa.2026.131845_b0075","doi-asserted-by":"crossref","unstructured":"Davis, J., & Goadrich, M. (2006, June). The relationship between Precision-Recall and ROC curves. In Proceedings of the 23rd international conference on Machine learning (pp. 233-240). DOI: 10.1145\/1143844.1143874.","DOI":"10.1145\/1143844.1143874"},{"key":"10.1016\/j.eswa.2026.131845_b0080","first-page":"255","article-title":"Keel data-mining software tool: Dataset repository, integration of algorithms and experimental analysis framework","volume":"17","author":"Derrac","year":"2015","journal-title":"Journal of Multiple-Valued Logic and Soft Computing"},{"key":"10.1016\/j.eswa.2026.131845_b0085","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.eswa.2017.03.073","article-title":"Self-organizing map oversampling (SOMO) for imbalanced dataset learning","volume":"82","author":"Douzas","year":"2017","journal-title":"Expert Systems with Applications"},{"issue":"8","key":"10.1016\/j.eswa.2026.131845_b0090","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.eswa.2026.131845_b0095","first-page":"1189","article-title":"Greedy function approximation: A gradient boosting machine","author":"Friedman","year":"2001","journal-title":"Annals of Statistics"},{"issue":"200","key":"10.1016\/j.eswa.2026.131845_b0100","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1080\/01621459.1937.10503522","article-title":"The use of ranks to avoid the assumption of normality implicit in the analysis of variance","volume":"32","author":"Friedman","year":"1937","journal-title":"Journal of the American Statistical Association"},{"key":"10.1016\/j.eswa.2026.131845_b0105","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","article-title":"Learning from class-imbalanced data: Review of methods and applications","volume":"73","author":"Haixiang","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131845_b0110","doi-asserted-by":"crossref","unstructured":"Han, H., Wang, W. Y., & Mao, B. H. (2005, August). Borderline-SMOTE: a new over-sampling method in imbalanced datasets learning. In International conference on intelligent computing (pp. 878-887). Berlin, Heidelberg: Springer Berlin Heidelberg. DOI: 10.1007\/11538059_91.","DOI":"10.1007\/11538059_91"},{"issue":"9","key":"10.1016\/j.eswa.2026.131845_b0115","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 Transactions on Knowledge and Data Engineering"},{"key":"10.1016\/j.eswa.2026.131845_b0120","first-page":"65","article-title":"A simple sequentially rejective multiple test procedure","volume":"6","author":"Holm","year":"1979","journal-title":"Scandinavian Journal of Statistics"},{"issue":"5","key":"10.1016\/j.eswa.2026.131845_b0125","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"},{"issue":"2","key":"10.1016\/j.eswa.2026.131845_b0130","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1177\/14759217241248209","article-title":"A rolling bearing fault diagnosis method based on interactive generative feature space oversampling-based autoencoder under imbalanced data","volume":"24","author":"Huang","year":"2025","journal-title":"Structural Health Monitoring"},{"key":"10.1016\/j.eswa.2026.131845_b0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2024.111368","article-title":"Imbalanced credit card fraud detection data: A solution based on hybrid neural network and clustering-based undersampling technique","volume":"154","author":"Huang","year":"2024","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2026.131845_b0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.108288","article-title":"KNNOR: An oversampling technique for imbalanced datasets","volume":"115","author":"Islam","year":"2022","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2026.131845_b0145","series-title":"Findings of the Association for Computational Linguistics: ACL 2023","first-page":"6810","article-title":"July). \u201cLow-Resource\u201d Text Classification: A Parameter-Free Classification Method with Compressors","author":"Jiang","year":"2023"},{"key":"10.1016\/j.eswa.2026.131845_b0150","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.119733","article-title":"A semi-supervised resampling method for class-imbalanced learning","volume":"221","author":"Jiang","year":"2023","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.131845_b0155","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1016\/S0304-3975(98)00075-9","article-title":"On tables of random numbers","volume":"207","author":"Kolmogorov","year":"1998","journal-title":"Theoretical Computer Science"},{"key":"10.1016\/j.eswa.2026.131845_b0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105662","article-title":"An empirical comparison and evaluation of minority oversampling techniques on a large number of imbalanced datasets","volume":"83","author":"Kov\u00e1cs","year":"2019","journal-title":"Applied Soft Computing"},{"issue":"4","key":"10.1016\/j.eswa.2026.131845_b0165","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":"Progress in Artificial Intelligence"},{"issue":"12","key":"10.1016\/j.eswa.2026.131845_b0170","doi-asserted-by":"crossref","first-page":"3250","DOI":"10.1109\/TIT.2004.838101","article-title":"The similarity metric","volume":"50","author":"Li","year":"2004","journal-title":"IEEE Transactions on Information Theory"},{"key":"10.1016\/j.eswa.2026.131845_b0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.109938","article-title":"Rolling bearing fault diagnosis under data imbalance and variable speed based on adaptive clustering weighted oversampling","volume":"244","author":"Li","year":"2024","journal-title":"Reliability Engineering & System Safety"},{"key":"10.1016\/j.eswa.2026.131845_b0180","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.ins.2022.11.108","article-title":"Subspace-based minority oversampling for imbalance classification","volume":"621","author":"Li","year":"2023","journal-title":"Information Sciences"},{"key":"10.1016\/j.eswa.2026.131845_b0185","doi-asserted-by":"crossref","first-page":"7426","DOI":"10.1109\/TMM.2024.3368910","article-title":"Learning with imbalanced noisy data by preventing bias in sample selection","volume":"26","author":"Liu","year":"2024","journal-title":"IEEE Transactions on Multimedia"},{"issue":"3","key":"10.1016\/j.eswa.2026.131845_b0190","doi-asserted-by":"crossref","first-page":"1434","DOI":"10.1007\/s42235-025-00673-0","article-title":"Swarm-based cost-sensitive decision tree using optimized rules for imbalanced data classification","volume":"22","author":"Mansouri","year":"2025","journal-title":"Journal of Bionic Engineering"},{"key":"10.1016\/j.eswa.2026.131845_b0195","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1007\/s10618-012-0295-5","article-title":"Training and assessing classification rules with imbalanced data","volume":"28","author":"Menardi","year":"2014","journal-title":"Data Mining and Knowledge Discovery"},{"key":"10.1016\/j.eswa.2026.131845_b0200","series-title":"An introduction to genetic algorithms","author":"Mitchell","year":"1998"},{"key":"10.1016\/j.eswa.2026.131845_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110886","article-title":"Imbalanced ensemble learning leveraging a novel data-level diversity metric","volume":"157","author":"Pang","year":"2025","journal-title":"Pattern Recognition"},{"key":"10.1016\/j.eswa.2026.131845_b0210","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"10.1016\/j.eswa.2026.131845_b0215","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.ins.2017.04.046","article-title":"Noise reduction a priori synthetic over-sampling for class imbalanced datasets","volume":"408","author":"Rivera","year":"2017","journal-title":"Information Sciences"},{"key":"10.1016\/j.eswa.2026.131845_b0220","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ins.2014.08.051","article-title":"SMOTE\u2013IPF: Addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering","volume":"291","author":"S\u00e1ez","year":"2015","journal-title":"Information Sciences"},{"issue":"8","key":"10.1016\/j.eswa.2026.131845_b0225","doi-asserted-by":"crossref","first-page":"6207","DOI":"10.1007\/s10462-022-10150-3","article-title":"On the joint-effect of class imbalance and overlap: A critical review","volume":"55","author":"Santos","year":"2022","journal-title":"Artificial Intelligence Review"},{"key":"10.1016\/j.eswa.2026.131845_b0230","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1023\/A:1022648800760","article-title":"The strength of weak learnability","volume":"5","author":"Schapire","year":"1990","journal-title":"Machine Learning"},{"key":"10.1016\/j.eswa.2026.131845_b0235","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.ins.2022.12.046","article-title":"A hybrid imbalanced classification model based on data density","volume":"624","author":"Shi","year":"2023","journal-title":"Information Sciences"},{"key":"10.1016\/j.eswa.2026.131845_b0240","article-title":"Towards unbalanced multiclass intrusion detection with hybrid sampling methods and ensemble classification","volume":"157","author":"Shin","year":"2024","journal-title":"Applied Soft Computing"},{"issue":"04","key":"10.1016\/j.eswa.2026.131845_b0245","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":"International Journal of Pattern Recognition and Artificial Intelligence"},{"key":"10.1016\/j.eswa.2026.131845_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.123328","article-title":"Undersampling method based on minority class density for imbalanced data","volume":"249","author":"Sun","year":"2024","journal-title":"Expert Systems with Applications"},{"issue":"1","key":"10.1016\/j.eswa.2026.131845_b0255","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1186\/s40537-024-00886-w","article-title":"Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction","volume":"11","author":"Talukder","year":"2024","journal-title":"Journal of Big Data"},{"issue":"4","key":"10.1016\/j.eswa.2026.131845_b0260","doi-asserted-by":"crossref","first-page":"2451","DOI":"10.1109\/TIT.2011.2110130","article-title":"Information distance in multiples","volume":"57","author":"Vit\u00e1nyi","year":"2011","journal-title":"IEEE Transactions on Information Theory"},{"key":"10.1016\/j.eswa.2026.131845_b0265","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ins.2019.08.062","article-title":"Neighbourhood-based undersampling approach for handling imbalanced and overlapped data","volume":"509","author":"Vuttipittayamongkol","year":"2020","journal-title":"Information Sciences"},{"key":"10.1016\/j.eswa.2026.131845_b0270","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124860","article-title":"Adaptive bagging-based dynamic ensemble selection in nonstationary environments","volume":"255","author":"Wei","year":"2024","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.131845_b0275","doi-asserted-by":"crossref","first-page":"2450","DOI":"10.1109\/TNSRE.2024.3421648","article-title":"Evaluation of acupuncture efficacy in modulating brain activity with periodic-aperiodic EEG measurements","volume":"32","author":"Yu","year":"2024","journal-title":"IEEE Transactions on Neural Systems and Rehabilitation Engineering"},{"issue":"6","key":"10.1016\/j.eswa.2026.131845_b0280","doi-asserted-by":"crossref","first-page":"4147","DOI":"10.1109\/JBHI.2025.3530922","article-title":"Neural manifold decoder for acupuncture stimulations with representation learning: An acupuncture-brain interface","volume":"29","author":"Yu","year":"2025","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"10.1016\/j.eswa.2026.131845_b0285","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2024.128091","article-title":"A novel gradient boosting approach for imbalanced regression","volume":"601","author":"Zhang","year":"2024","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.131845_b0290","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.inffus.2022.12.014","article-title":"TSK fuzzy system fusion at sensitivity-ensemble-level for imbalanced data classification","volume":"92","author":"Zhang","year":"2023","journal-title":"Information Fusion"},{"key":"10.1016\/j.eswa.2026.131845_b0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.future.2025.107750","article-title":"UFIDSF: An undersampling approach based on feature importance and double side filter for imbalanced data classification","volume":"167","author":"Zheng","year":"2025","journal-title":"Future Generation Computer Systems"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742600758X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S095741742600758X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:45:07Z","timestamp":1778114707000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S095741742600758X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":58,"alternative-id":["S095741742600758X"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131845","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"KCPIA: Kolmogorov complexity-based positive instance augmentation for class-imbalance problem","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131845","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"131845"}}