{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:55:08Z","timestamp":1769007308729,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T00:00:00Z","timestamp":1580256000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T00:00:00Z","timestamp":1580256000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71420107025"],"award-info":[{"award-number":["71420107025"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2020,8]]},"DOI":"10.1007\/s00521-020-04747-4","type":"journal-article","created":{"date-parts":[[2020,1,29]],"date-time":"2020-01-29T20:03:37Z","timestamp":1580328217000},"page":"13035-13049","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["A density weighted fuzzy outlier clustering approach for class imbalanced learning"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9442-5648","authenticated-orcid":false,"given":"Xiaokang","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huiwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,1,29]]},"reference":[{"issue":"5","key":"4747_CR1","doi-asserted-by":"publisher","first-page":"1385","DOI":"10.1007\/s00726-010-0595-2","volume":"39","author":"A Anand","year":"2010","unstructured":"Anand A, Pugalenthi G, Fogel GB, Suganthan PN (2010) An approach for classification of highly imbalanced data using weighting and undersampling. Amino Acids 39(5):1385\u20131391. https:\/\/doi.org\/10.1007\/s00726-010-0595-2","journal-title":"Amino Acids"},{"issue":"2","key":"4747_CR2","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1109\/TKDE.2012.232","volume":"26","author":"S Barua","year":"2014","unstructured":"Barua S, Islam MM, Yao X, Murase K (2014) Mwmote-majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405\u2013425. https:\/\/doi.org\/10.1109\/TKDE.2012.232","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"4747_CR3","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/978-1-4757-0450-1","volume-title":"Pattern recognition with fuzzy objective function algorithms","author":"JC Bezdek","year":"1981","unstructured":"Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Springer, Boston, pp 95\u2013154"},{"issue":"3","key":"4747_CR4","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1007\/s10489-011-0287-y","volume":"36","author":"C Bunkhumpornpat","year":"2012","unstructured":"Bunkhumpornpat C, Sinapiromsaran K, Lursinsap C (2012) Dbsmote: density-based synthetic minority over-sampling technique. Appl Intell 36(3):664\u2013684. https:\/\/doi.org\/10.1007\/s10489-011-0287-y","journal-title":"Appl Intell"},{"issue":"1","key":"4747_CR5","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.eswa.2012.07.021","volume":"40","author":"ME Celebi","year":"2013","unstructured":"Celebi ME, Kingravi HA, Vela PA (2013) A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst Appl 40(1):200\u2013210. https:\/\/doi.org\/10.1016\/j.eswa.2012.07.021","journal-title":"Expert Syst Appl"},{"issue":"1","key":"4747_CR6","first-page":"321","volume":"16","author":"NV Chawla","year":"2011","unstructured":"Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2011) Smote: synthetic minority over-sampling technique. J Artif Intell Res 16(1):321\u2013357","journal-title":"J Artif Intell Res"},{"issue":"1","key":"4747_CR7","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s00521-011-0616-4","volume":"21","author":"I Dagher","year":"2012","unstructured":"Dagher I (2012) Clustering with complex centers. Neural Comput Appl 21(1):133\u2013144. https:\/\/doi.org\/10.1007\/s00521-011-0616-4","journal-title":"Neural Comput Appl"},{"key":"4747_CR8","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/j.patrec.2016.10.006","volume":"93","author":"D Devi","year":"2017","unstructured":"Devi D, Biswas S, Purkayastha B (2017) Redundancy-driven modified tomek-link based undersampling: a solution to class imbalance. Pattern Recogni Lett 93:3\u201312. https:\/\/doi.org\/10.1016\/j.patrec.2016.10.006","journal-title":"Pattern Recogni Lett"},{"key":"4747_CR9","unstructured":"Drummond C, Holte R (2003) C4.5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. In: Workshop on learning from imbalanced datasets II, held in conjunction with ICML 2003"},{"issue":"7","key":"4747_CR10","doi-asserted-by":"publisher","first-page":"1131","DOI":"10.1007\/s13042-017-0636-1","volume":"9","author":"M Du","year":"2018","unstructured":"Du M, Ding S, Xue Y (2018) A robust density peaks clustering algorithm using fuzzy neighborhood. Int J Mach Learn Cybern 9(7):1131\u20131140. https:\/\/doi.org\/10.1007\/s13042-017-0636-1","journal-title":"Int J Mach Learn Cybern"},{"key":"4747_CR11","unstructured":"Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second international conference on knowledge discovery and data mining, KDD\u201996. AAAI Press, pp 226\u2013231. http:\/\/dl.acm.org\/citation.cfm?id=3001460.3001507"},{"key":"4747_CR12","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, Bing G (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220\u2013239. https:\/\/doi.org\/10.1016\/j.eswa.2016.12.035","journal-title":"Expert Syst Appl"},{"issue":"1","key":"4747_CR13","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1145\/1656274.1656278","volume":"11","author":"M Hall","year":"2009","unstructured":"Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. ACM SIGKDD Explor Newsl 11(1):10\u201318","journal-title":"ACM SIGKDD Explor Newsl"},{"key":"4747_CR14","doi-asserted-by":"publisher","first-page":"878","DOI":"10.1007\/11538059_91","volume-title":"Advances in intelligent computing","author":"H Han","year":"2005","unstructured":"Han H, Wang WY, Mao BH (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: Huang DS, Zhang XP, Huang GB (eds) Advances in intelligent computing. Springer, Berlin, pp 878\u2013887"},{"issue":"9","key":"4747_CR15","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1016\/S0167-8655(03)00003-5","volume":"24","author":"Z He","year":"2003","unstructured":"He Z, Xu X, Deng S (2003) Discovering cluster-based local outliers. Pattern Recognit Lett 24(9):1641\u20131650. https:\/\/doi.org\/10.1016\/S0167-8655(03)00003-5","journal-title":"Pattern Recognit Lett"},{"issue":"5","key":"4747_CR16","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1109\/TPAMI.2005.95","volume":"27","author":"JZ Huang","year":"2005","unstructured":"Huang JZ, Ng MK, Rong H, Li Z (2005) Automated variable weighting in k-means type clustering. IEEE Trans Pattern Anal Mach Intell 27(5):657\u2013668. https:\/\/doi.org\/10.1109\/TPAMI.2005.95","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"8","key":"4747_CR17","doi-asserted-by":"publisher","first-page":"1433","DOI":"10.1109\/TNNLS.2013.2293795","volume":"25","author":"X Huang","year":"2014","unstructured":"Huang X, Ye Y, Zhang H (2014) Extensions of kmeans-type algorithms: a new clustering framework by integrating intracluster compactness and intercluster separation. IEEE Trans Neural Netw Learn Syst 25(8):1433\u20131446. https:\/\/doi.org\/10.1109\/TNNLS.2013.2293795","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"4747_CR18","doi-asserted-by":"crossref","unstructured":"Keller A (2000) Fuzzy clustering with outliers. In: Proceedings of the NAFIPS00 2000, pp 143\u2013147","DOI":"10.1109\/NAFIPS.2000.877408"},{"key":"4747_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04035-w","author":"H Khanali","year":"2019","unstructured":"Khanali H, Vaziri B (2019) An improved approach to fuzzy clustering based on fcm algorithm and extended vikor method. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04035-w","journal-title":"Neural Comput Appl"},{"key":"4747_CR20","doi-asserted-by":"publisher","first-page":"714","DOI":"10.1016\/j.asoc.2015.08.060","volume":"38","author":"B Krawczyk","year":"2016","unstructured":"Krawczyk B, Galar M, Jele ukasz, Herrera F (2016) Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Appl Soft Comput 38:714\u2013726. https:\/\/doi.org\/10.1016\/j.asoc.2015.08.060","journal-title":"Appl Soft Comput"},{"key":"4747_CR21","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.ins.2017.05.008","volume":"409","author":"WC Lin","year":"2017","unstructured":"Lin WC, Tsai CF, Hu YH, Jhang JS (2017) Clustering-based undersampling in class-imbalanced data. Inf Sci 409:17\u201326. https:\/\/doi.org\/10.1016\/j.ins.2017.05.008","journal-title":"Inf Sci"},{"key":"4747_CR22","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.fss.2014.01.015","volume":"258","author":"V Lopez","year":"2015","unstructured":"Lopez V, del Rio S, Benitez JM, Herrera F (2015) Cost-sensitive linguistic fuzzy rule based classification systems under the mapreduce framework for imbalanced big data. Fuzzy Sets Syst 258:5\u201338. https:\/\/doi.org\/10.1016\/j.fss.2014.01.015","journal-title":"Fuzzy Sets Syst"},{"key":"4747_CR23","doi-asserted-by":"publisher","unstructured":"Majhi SK (2019) Fuzzy clustering algorithm based on modified whale optimization algorithm for automobile insurance fraud detection. In: Evolutionary intelligence, pp 1\u201312. https:\/\/doi.org\/10.1007\/s12065-019-00260-3","DOI":"10.1007\/s12065-019-00260-3"},{"key":"4747_CR24","doi-asserted-by":"publisher","first-page":"88","DOI":"10.1016\/j.neucom.2017.03.011","volume":"243","author":"N Ofek","year":"2017","unstructured":"Ofek N, Rokach L, Stern R, Shabtai A (2017) Fast-CBUS: a fast clustering-based undersampling method for addressing the class imbalance problem. Neurocomputing 243:88\u2013102. https:\/\/doi.org\/10.1016\/j.neucom.2017.03.011","journal-title":"Neurocomputing"},{"issue":"6191","key":"4747_CR25","doi-asserted-by":"publisher","first-page":"1492","DOI":"10.1126\/science.1242072","volume":"344","author":"A Rodriguez","year":"2014","unstructured":"Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492\u20131496","journal-title":"Science"},{"key":"4747_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04049-4","author":"GRL Silva","year":"2019","unstructured":"Silva GRL, Neto PC, Torres LCB, Braga AP (2019) A fuzzy data reduction cluster method based on boundary information for large datasets. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-019-04049-4","journal-title":"Neural Comput Appl"},{"issue":"1","key":"4747_CR27","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s00521-018-3633-8","volume":"31","author":"A Somasundaram","year":"2019","unstructured":"Somasundaram A, Reddy S (2019) Parallel and incremental credit card fraud detection model to handle concept drift and data imbalance. Neural Comput Appl 31(1):3\u201314. https:\/\/doi.org\/10.1007\/s00521-018-3633-8","journal-title":"Neural Comput Appl"},{"key":"4747_CR28","doi-asserted-by":"crossref","unstructured":"Somasundaram A, Reddy US (2017) Modelling a stable classifier for handling large scale data with noise and imbalance. In: Proceedings of the 2017 international conference on computational intelligence in data science (ICCIDS) Chennai, India, pp 16","DOI":"10.1109\/ICCIDS.2017.8272643"},{"issue":"21","key":"4747_CR29","doi-asserted-by":"publisher","first-page":"7541","DOI":"10.1016\/j.eswa.2015.05.014","volume":"42","author":"A Stetco","year":"2015","unstructured":"Stetco A, Zeng XJ, Keane J (2015) Fuzzy c-means++: fuzzy c-means with effective seeding initialization. Expert Syst Appl 42(21):7541\u20137548. https:\/\/doi.org\/10.1016\/j.eswa.2015.05.014","journal-title":"Expert Syst Appl"},{"key":"4747_CR30","volume-title":"Exploratory data analysis","author":"J Tukey","year":"1977","unstructured":"Tukey J (1977) Exploratory data analysis. Addison-Wesley Publishing Company, Menlo Park"},{"issue":"12","key":"4747_CR31","doi-asserted-by":"publisher","first-page":"85838592","DOI":"10.1007\/s00521-019-04089-w","volume":"31","author":"T Vo","year":"2019","unstructured":"Vo T, Nguyen T, Le CT (2019) A hybrid framework for smile detection in class imbalance scenarios. Neural Comput Appl 31(12):85838592","journal-title":"Neural Comput Appl"},{"issue":"3, Part 1","key":"4747_CR32","doi-asserted-by":"publisher","first-page":"5718","DOI":"10.1016\/j.eswa.2008.06.108","volume":"36","author":"SJ Yen","year":"2009","unstructured":"Yen SJ, Lee YS (2009) Cluster-based under-sampling approaches for imbalanced data distributions. Expert Syst Appl 36(3, Part 1):5718\u20135727. https:\/\/doi.org\/10.1016\/j.eswa.2008.06.108","journal-title":"Expert Syst Appl"},{"issue":"3","key":"4747_CR33","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1007\/s00521-007-0089-7","volume":"16","author":"K Yoon","year":"2007","unstructured":"Yoon K, Kwek S (2007) A data reduction approach for resolving the imbalanced data issue in functional genomics. Neural Comput Appl 16(3):295\u2013306. https:\/\/doi.org\/10.1007\/s00521-007-0089-7","journal-title":"Neural Comput Appl"},{"key":"4747_CR34","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.knosys.2014.12.007","volume":"76","author":"H Yu","year":"2015","unstructured":"Yu H, Mu C, Sun C, Yang W, Zuo X (2015) Support vector machine-based optimized decision threshold adjustment strategy for classifying imbalanced data. Knowl Based Syst 76:67\u201378","journal-title":"Knowl Based Syst"},{"issue":"11","key":"4747_CR35","doi-asserted-by":"publisher","first-page":"5304","DOI":"10.1109\/TNNLS.2018.2797060","volume":"29","author":"H Zhang","year":"2018","unstructured":"Zhang H, Wang S, Xu X, Chow TWS, Wu QMJ (2018) Tree2vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 29(11):5304\u20135318. https:\/\/doi.org\/10.1109\/TNNLS.2018.2797060","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04747-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00521-020-04747-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-020-04747-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:37:14Z","timestamp":1611794234000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00521-020-04747-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,29]]},"references-count":35,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2020,8]]}},"alternative-id":["4747"],"URL":"https:\/\/doi.org\/10.1007\/s00521-020-04747-4","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,29]]},"assertion":[{"value":"29 July 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 January 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 January 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Compliance with ethical standards"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}