{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T05:52:55Z","timestamp":1772689975869,"version":"3.50.1"},"reference-count":35,"publisher":"Tech Science Press","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["CMC"],"published-print":{"date-parts":[[2025]]},"DOI":"10.32604\/cmc.2025.063465","type":"journal-article","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T03:22:27Z","timestamp":1745896947000},"page":"5699-5727","source":"Crossref","is-referenced-by-count":1,"title":["Neighbor Displacement-Based Enhanced Synthetic Oversampling for Multiclass Imbalanced Data"],"prefix":"10.32604","volume":"83","author":[{"given":"I Made","family":"Putrama","sequence":"first","affiliation":[]},{"given":"P\u00e9ter","family":"Martinek","sequence":"additional","affiliation":[]}],"member":"17807","published-online":{"date-parts":[[2025]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"5059","DOI":"10.1016\/j.jksuci.2022.06.005","article-title":"RN-SMOTE: reduced noise SMOTE based on DBSCAN for enhancing imbalanced data classification","volume":"34","author":"Arafa","year":"2022","journal-title":"J King Saud Univ\u2014Comput Inf Sci"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"108992","DOI":"10.1016\/j.patcog.2022.108992","article-title":"Grouping-based oversampling in kernel space for imbalanced data classification","volume":"133","author":"Ren","year":"2023","journal-title":"Pattern Recognit"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"120555","DOI":"10.1016\/j.ins.2024.120555","article-title":"Dynamic classification ensembles for handling imbalanced multiclass drifted data streams","volume":"670","author":"Madkour","year":"2024","journal-title":"Inf Sci"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"116962","DOI":"10.1016\/j.eswa.2022.116962","article-title":"What makes multi-class imbalanced problems difficult? An experimental study","volume":"199","author":"Lango","year":"2022","journal-title":"Expert Syst Appl"},{"key":"ref5","first-page":"1","article-title":"Classification of imbalanced data set in financial field based on combined algorithm","volume":"2022","author":"Yu","year":"2022","journal-title":"Mob Inf Syst"},{"key":"ref6","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.isatra.2019.08.012","article-title":"Deep transfer network with joint distribution adaptation: a new intelligent fault diagnosis framework for industry application","volume":"97","author":"Han","year":"2020","journal-title":"ISA Trans"},{"key":"ref7","doi-asserted-by":"crossref","first-page":"2518","DOI":"10.1109\/TII.2021.3100284","article-title":"Imbalanced sample selection with deep reinforcement learning for fault diagnosis","volume":"18","author":"Fan","year":"2022","journal-title":"IEEE Trans Ind Inform"},{"key":"ref8","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1016\/j.jmsy.2023.10.014","article-title":"Systematic review of class imbalance problems in manufacturing","volume":"71","author":"Giorgio","year":"2023","journal-title":"J Manuf Syst"},{"key":"ref9","doi-asserted-by":"crossref","first-page":"110415","DOI":"10.1016\/j.asoc.2023.110415","article-title":"A broad review on class imbalance learning techniques","volume":"143","author":"Rezvani","year":"2023","journal-title":"Appl Soft Comput"},{"key":"ref10","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1016\/j.ins.2020.12.058","article-title":"A modified real-value negative selection detector-based oversampling approach for multiclass imbalance problems","volume":"556","author":"Liu","year":"2021","journal-title":"Inf Sci"},{"key":"ref11","series-title":"Proceedings of the 2nd International Conference on Computing Advancements","first-page":"485","article-title":"Class imbalance problems in machine learning: a review of methods and future challenges","author":"Niaz","year":"2022 Mar 10\u201312"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ipm.2022.103235","article-title":"RGAN-EL: a GAN and ensemble learning-based hybrid approach for imbalanced data classification","volume":"60","author":"Ding","year":"2023","journal-title":"Inf Process Manag"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isatra.2021.11.040","article-title":"Generative adversarial network in mechanical fault diagnosis under small sample: a systematic review on applications and future perspectives","volume":"128","author":"Pan","year":"2022","journal-title":"ISA Trans"},{"key":"ref14","doi-asserted-by":"crossref","first-page":"3759","DOI":"10.1007\/s42417-022-00781-9","article-title":"Augmentation of decision tree model through hyper-parameters tuning for monitoring of cutting tool faults based on vibration signatures","volume":"11","author":"Patange","year":"2023","journal-title":"J Vib Eng Technol"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"109975","DOI":"10.1016\/j.ymssp.2022.109975","article-title":"Impact of noise model on the performance of algorithms for fault diagnosis in rolling bearings","volume":"188","author":"Pancaldi","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"ref16","first-page":"1703","article-title":"Data-driven decision-making for bank targetmarketing using supervised learning classifiers on imbalanced big data","volume":"81","author":"Nasir","year":"2024","journal-title":"Comput Mater Contin"},{"key":"ref17","doi-asserted-by":"crossref","first-page":"107211","DOI":"10.1016\/j.engappai.2023.107211","article-title":"Iterative minority oversampling and its ensemble for ordinal imbalanced datasets","volume":"127","author":"Wang","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"ref18","doi-asserted-by":"crossref","first-page":"123149","DOI":"10.1016\/j.eswa.2024.123149","article-title":"Efficient hybrid oversampling and intelligent undersampling for imbalanced big data classification","volume":"246","author":"Vairetti","year":"2024","journal-title":"Expert Syst Appl"},{"key":"ref19","series-title":"Proceedings of the International Conference on Machine Learning (ICML 2003)","first-page":"1","article-title":"KNN approach to unbalanced data distributions: a case study involving information extraction","author":"Zhang","year":"2003 Aug 21\u201324"},{"key":"ref20","first-page":"83","article-title":"Foundations of data imbalance and solutions for a data democracy","author":"Kulkarni","year":"2020","journal-title":"Data Democr Nexus Artif Intell Softw Dev Knowl Eng"},{"key":"ref21","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":"ref22","doi-asserted-by":"crossref","first-page":"106911","DOI":"10.1016\/j.engappai.2023.106911","article-title":"Review of resampling techniques for the treatment of imbalanced industrial data classification in equipment condition monitoring","volume":"126","author":"Yuan","year":"2023","journal-title":"Eng Appl Artif Intell"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"110895","DOI":"10.1016\/j.asoc.2023.110895","article-title":"Synthetic minority oversampling using edited displacement-based k-nearest neighbors","volume":"148","author":"Wang","year":"2023","journal-title":"Appl Soft Comput"},{"key":"ref24","doi-asserted-by":"crossref","first-page":"106157","DOI":"10.1016\/j.neunet.2024.106157","article-title":"Enhancing and improving the performance of imbalanced class data using novel GBO and SSG: a comparative analysis","volume":"173","author":"Ahsan","year":"2024","journal-title":"Neural Netw"},{"key":"ref25","series-title":"Proceedings of the 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)","first-page":"132","article-title":"ADASYN: adaptive synthetic sampling approach for imbalanced learning","author":"He","year":"2008 Jun 1\u20138"},{"key":"ref26","doi-asserted-by":"crossref","first-page":"108288","DOI":"10.1016\/j.asoc.2021.108288","article-title":"KNNOR: an oversampling technique for imbalanced datasets","volume":"115","author":"Islam","year":"2022","journal-title":"Appl Soft Comput"},{"key":"ref27","first-page":"431","author":"Wang","year":"2022","journal-title":"Advanced Data Mining and Applications. International Conference on Advanced Data Mining and Applications; 2022 Nov 28\u201330"},{"key":"ref28","doi-asserted-by":"crossref","first-page":"111376","DOI":"10.1016\/j.asoc.2024.111376","article-title":"R-WDLS: an efficient security region oversampling technique based on data distribution","volume":"154","author":"Jia","year":"2024","journal-title":"Appl Soft Comput"},{"key":"ref29","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"Dem\u0161ar","year":"2006","journal-title":"J Mach Learn Res"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"128521","DOI":"10.1109\/ACCESS.2021.3102643","article-title":"Electricity theft detection with automatic labeling and enhanced RUSBoost classification using differential evolution and jaya algorithm","volume":"9","author":"Mujeeb","year":"2021","journal-title":"IEEE Access"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"111368","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":"Appl Soft Comput"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"112947","DOI":"10.1016\/j.knosys.2024.112947","article-title":"A dynamic ensemble learning based data mining framework for medical imbalanced big data","volume":"310","author":"Rithani","year":"2025","journal-title":"Knowl Based Syst"},{"key":"ref33","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s40537-020-00390-x","article-title":"Resampling imbalanced data for network intrusion detection datasets","volume":"8","author":"Bagui","year":"2021","journal-title":"J Big Data"},{"key":"ref34","doi-asserted-by":"crossref","first-page":"6583","DOI":"10.1007\/s11227-022-04908-3","article-title":"Improved multi-class classification approach for imbalanced big data on spark","volume":"79","author":"Singh","year":"2023","journal-title":"J Supercomput"},{"key":"ref35","doi-asserted-by":"crossref","first-page":"3657","DOI":"10.1007\/s11276-021-02552-y","article-title":"Distributed classification for imbalanced big data in distributed environments","volume":"30","author":"Wang","year":"2024","journal-title":"Wirel Netw"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-83-3\/TSP_CMC_63465\/TSP_CMC_63465.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:29:18Z","timestamp":1763342958000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v83n3\/61046"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":35,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.063465","relation":{},"ISSN":["1546-2226"],"issn-type":[{"value":"1546-2226","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]}}}