{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:42:40Z","timestamp":1762522960039,"version":"3.41.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"5-6","license":[{"start":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T00:00:00Z","timestamp":1735257600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2025,6]]},"DOI":"10.1007\/s13042-024-02470-3","type":"journal-article","created":{"date-parts":[[2024,12,27]],"date-time":"2024-12-27T16:34:17Z","timestamp":1735317257000},"page":"3629-3661","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A new supervised outlier detection method for hybrid data"],"prefix":"10.1007","volume":"16","author":[{"given":"Danlu","family":"Feng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaowen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinjin","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,27]]},"reference":[{"key":"2470_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-94-015-3994-4","volume-title":"Identification of outliers","author":"DM Hawkins","year":"1980","unstructured":"Hawkins DM (1980) Identification of outliers. Chapman and Hall, London"},{"key":"2470_CR2","doi-asserted-by":"crossref","unstructured":"Ng R (2013) Outlier detection in personalized medicine. In: Proceedings of the ACM SIGKDD Workshop on outlier detection and description, 2013","DOI":"10.1145\/2500853.2500856"},{"key":"2470_CR3","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.inffus.2019.02.006","volume":"51","author":"B Wang","year":"2019","unstructured":"Wang B, Mao Z (2019) Outlier detection based on a dynamic ensemble model: applied to process monitoring. Inform Fusion 51:244\u2013258","journal-title":"Inform Fusion"},{"key":"2470_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119153","volume":"642","author":"XD Wang","year":"2023","unstructured":"Wang XD, Liu ZL, Liu JM, Liu JY (2023) Fraud detection on multi-relation graphs via imbalanced and interactive learning. Inf Sci 642:119153","journal-title":"Inf Sci"},{"key":"2470_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.119562","volume":"217","author":"H Fanai","year":"2023","unstructured":"Fanai H, Abbasimehr H (2023) A novel combined approach based on deep autoencoder and deep classifiers for credit card fraud detection. Expert Syst Appl 217:119562","journal-title":"Expert Syst Appl"},{"key":"2470_CR6","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1016\/j.asoc.2018.12.029","volume":"76","author":"W Biao","year":"2019","unstructured":"Biao W, Mao ZZ (2019) Outlier detection based on Gaussian process with application to industrial processes. Appl Soft Comput 76:505\u2013516","journal-title":"Appl Soft Comput"},{"key":"2470_CR7","doi-asserted-by":"crossref","unstructured":"Zhao Y, Hryniewicki MK (2018) XGBOD: improving supervised outlier detection with unsupervised representation learning. In: 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brazil, July 8-13, 2018, IEEE, 2018, pp. 1\u20138","DOI":"10.1109\/IJCNN.2018.8489605"},{"key":"2470_CR8","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.neucom.2022.02.047","volume":"486","author":"\u00c1 Fern\u00e1ndez","year":"2022","unstructured":"Fern\u00e1ndez \u00c1, Bella J, Dorronsoro JR (2022) Supervised outlier detection for classification and regression. Neurocomputing 486:77\u201392","journal-title":"Neurocomputing"},{"issue":"1","key":"2470_CR9","first-page":"8501683","volume":"2017","author":"H Song","year":"2017","unstructured":"Song H, Jiang Z, Men A, Yang B (2017) A hybrid semi-supervised anomaly detection model for high-dimensional data. Comput Intel Neurosci 2017(1):8501683","journal-title":"Comput Intel Neurosci"},{"issue":"1","key":"2470_CR10","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.ijar.2013.03.012","volume":"55","author":"S Mascaro","year":"2014","unstructured":"Mascaro S, Nicholso AE, Korb KB (2014) Anomaly detection in vessel tracks using Bayesian networks. Int J Approx Reason 55(1):84\u201398","journal-title":"Int J Approx Reason"},{"key":"2470_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ijar.2020.12.003","volume":"130","author":"B Ali","year":"2021","unstructured":"Ali B, Azam N, Shah A, Yao JT (2021) A spatial filtering inspired three-way clustering approach with application to outlier detection. Int J Approx Reason 130:1\u201321","journal-title":"Int J Approx Reason"},{"issue":"9\u201310","key":"2470_CR12","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 Recogn Lett 24(9\u201310):1641\u20131650","journal-title":"Pattern Recogn Lett"},{"key":"2470_CR13","unstructured":"Johnson T, Kwok I, Ng RT (1998) Fast computation of 2-dimensional depth contours. In: International Conference on knowledge discovery and data mining, pp 224\u2013228"},{"issue":"3","key":"2470_CR14","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1007\/s007780050006","volume":"8","author":"EM Knorr","year":"2000","unstructured":"Knorr EM, Ng RT, Tucakov V (2000) Distance-based outliers: algorithms and applications. VLDB J 8(3):237\u2013253","journal-title":"VLDB J"},{"key":"2470_CR15","doi-asserted-by":"crossref","unstructured":"Breunig MM, Kriegel HP, RT, Sander J (2000) Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of data, pp 93\u2013104","DOI":"10.1145\/342009.335388"},{"key":"2470_CR16","doi-asserted-by":"crossref","unstructured":"Sakurada M, Yairi T (2014) Anomaly detection using autoencoders with nonlinear dimensionality reduction. In: Pacific Rim International Conference on Artificial Intelligence (PRICAI), Workshop on Machine Learning for Sensory Data Analysis (MLSDA), 2014","DOI":"10.1145\/2689746.2689747"},{"key":"2470_CR17","unstructured":"Ruff L, Vandermeulen R, Goernitz N, Deecke L, Siddiqui SA, Binder A, et al (2018) Deep one-class classification. In: International Conference on machine learning (pp. 4393-4402). PMLR, 2018"},{"key":"2470_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.displa.2024.102775","volume":"84","author":"Y Gao","year":"2024","unstructured":"Gao Y, Lin QQ, Ye S, Cheng Y, Zhang T, Liang B, Lu WN (2024) Outlier detection in temporal and spatial sequences via correlation analysis based on graph neural networks. Displays 84:102775","journal-title":"Displays"},{"key":"2470_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121161","volume":"236","author":"XS Du","year":"2024","unstructured":"Du XS, Chen JY, Yu J, Li S, Tan QY (2024) Generative adversarial nets for unsupervised outlier detection. Expert Syst Appl 236:121161","journal-title":"Expert Syst Appl"},{"key":"2470_CR20","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 Inform Sci 11:341\u2013356","journal-title":"Int J Comput Inform Sci"},{"issue":"4","key":"2470_CR21","doi-asserted-by":"publisher","first-page":"1217","DOI":"10.1007\/s13042-022-01695-4","volume":"14","author":"YZ Pan","year":"2023","unstructured":"Pan YZ, Xu WH, Ran QW (2023) An incremental approach to feature selection using the weighted dominance-based neighborhood rough sets. Int J Mach Learn Cybern 14(4):1217\u20131233","journal-title":"Int J Mach Learn Cybern"},{"key":"2470_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2024.123778","volume":"249","author":"YH Sun","year":"2024","unstructured":"Sun YH, Zhu P (2024) Online group streaming feature selection based on fuzzy neighborhood granular ball rough sets. Expert Syst Appl 249:123778","journal-title":"Expert Syst Appl"},{"key":"2470_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2024.100468","volume":"11","author":"KN Singh","year":"2024","unstructured":"Singh KN, Mantri JK (2024) An intelligent recommender system using machine learning association rules and rough set for disease prediction from incomplete symptom set. Decis Anal J 11:100468","journal-title":"Decis Anal J"},{"key":"2470_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2024.121016","volume":"678","author":"XY Su","year":"2024","unstructured":"Su XY, Yuan Z, Chen BY, Peng DZ, Chen HM, Chen YK (2024) Detecting anomalies with granular-ball fuzzy rough sets. Inf Sci 678:121016","journal-title":"Inf Sci"},{"key":"2470_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108198","volume":"133","author":"C Liu","year":"2024","unstructured":"Liu C, Peng DZ, Chen HM, Yuan Z (2024) Attribute granules-based object entropy for outlier detection in nominal data. Eng Appl Artif Intell 133:108198","journal-title":"Eng Appl Artif Intell"},{"key":"2470_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2024.112070","volume":"165","author":"BY Chen","year":"2024","unstructured":"Chen BY, Yuan Z, Peng DZ, Chen XL, Chen HM (2024) Consistency-guided semi-supervised outlier detection in heterogeneous data using fuzzy rough sets. Appl Soft Comput 165:112070","journal-title":"Appl Soft Comput"},{"issue":"8","key":"2470_CR27","doi-asserted-by":"publisher","first-page":"4285","DOI":"10.1109\/TFUZZ.2024.3393710","volume":"32","author":"Y Wu","year":"2024","unstructured":"Wu Y, Wang SH, Chen HM, Peng DZ, Yuan Z (2024) Kernelized fuzzy-rough anomaly detection. IEEE Trans Fuzzy Syst 32(8):4285\u20134296","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"11","key":"2470_CR28","doi-asserted-by":"publisher","first-page":"11800","DOI":"10.1109\/TITS.2023.3285615","volume":"24","author":"Q Chen","year":"2023","unstructured":"Chen Q, Xie LR, Zeng LR, Jiang SN, Ding WP, Huang XM, Wang H (2023) Neighborhood rough residual network-based outlier detection method in IoT-enabled maritime transportation systems. IEEE Trans Intell Transp Syst 24(11):11800\u201311811","journal-title":"IEEE Trans Intell Transp Syst"},{"issue":"5","key":"2470_CR29","doi-asserted-by":"publisher","first-page":"2082","DOI":"10.1109\/TKDE.2023.3312108","volume":"36","author":"XY Zhang","year":"2023","unstructured":"Zhang XY, Yuan Z, Miao DQ (2023) Outlier detection using three-way neighborhood characteristic regions and corresponding fusion measurement. IEEE Trans Knowl Data Eng 36(5):2082\u20132095","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2470_CR30","doi-asserted-by":"publisher","first-page":"710","DOI":"10.1016\/j.ins.2022.11.154","volume":"622","author":"L Gao","year":"2023","unstructured":"Gao L, Cai MJ, Li QG (2023) A relative granular ratio-based outlier detection method in heterogeneous data. Inf Sci 622:710\u2013731","journal-title":"Inf Sci"},{"key":"2470_CR31","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.ins.2023.03.037","volume":"633","author":"R Li","year":"2023","unstructured":"Li R, Chen HC, Liu SX, Li X, Li YL, Wang B (2023) Incomplete mixed data-driven outlier detection based on local-global neighborhood information. Inf Sci 633:204\u2013225","journal-title":"Inf Sci"},{"issue":"9","key":"2470_CR32","doi-asserted-by":"publisher","first-page":"2483","DOI":"10.1007\/s13042-018-0884-8","volume":"10","author":"F Jiang","year":"2018","unstructured":"Jiang F, Zhao HB, Du JW, Xue Y, Peng YJ (2018) Outlier detection based on approximation accuracy entropy. Int J Mach Learn Cybern 10(9):2483\u20132499","journal-title":"Int J Mach Learn Cybern"},{"key":"2470_CR33","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.ins.2021.02.045","volume":"564","author":"Y Wang","year":"2021","unstructured":"Wang Y, Li Y (2021) Outlier detection based on weighted neighbourhood information network for mixed-valued datasets. Inf Sci 564:396\u2013415","journal-title":"Inf Sci"},{"key":"2470_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102133","volume":"103","author":"BY Chen","year":"2024","unstructured":"Chen BY, Li YX, Peng DZ, Chen HM, Yuan Z (2024) Fusing multi-scale fuzzy information to detect outliers. Inform Fusion 103:102133","journal-title":"Inform Fusion"},{"key":"2470_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.119950","volume":"657","author":"Y Song","year":"2024","unstructured":"Song Y, Lin H, Li ZW (2024) Outlier detection in a multiset-valued information system based on rough set theory and granular computing. Inf Sci 657:119950","journal-title":"Inf Sci"},{"issue":"2","key":"2470_CR36","first-page":"3023","volume":"44","author":"ZW Zhao","year":"2023","unstructured":"Zhao ZW, Yang GT, Li ZW (2023) Outlier detection for incomplete real-valued data based on inner boundary. J Intell Fuzzy Syste 44(2):3023\u20133041","journal-title":"J Intell Fuzzy Syste"},{"key":"2470_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.109995","volume":"134","author":"Z Yuan","year":"2023","unstructured":"Yuan Z, Chen B, Liu J, Chen H, Peng D, Li D (2023) Anomaly detection based on weighted fuzzy-rough density. Appl Soft Comput 134:109995","journal-title":"Appl Soft Comput"},{"issue":"6","key":"2470_CR38","first-page":"1317","volume":"39","author":"Z Yuan","year":"2018","unstructured":"Yuan Z, Zhang XY, Feng S (2018) Sequence-based mixed attribute outlier detection in neighborhood rough sets. J Chin Comput Syst 39(6):1317\u20131322","journal-title":"J Chin Comput Syst"},{"key":"2470_CR39","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.eswa.2018.06.013","volume":"112","author":"Z Yuan","year":"2018","unstructured":"Yuan Z, Zhang XY, Feng S (2018) Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures. Expert Syst Appl 112:243\u2013257","journal-title":"Expert Syst Appl"},{"key":"2470_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.fss.2020.10.017","volume":"421","author":"Z Yuan","year":"2021","unstructured":"Yuan Z, Chen H, Li T, Liu J, Wang S (2021) Fuzzy information entropy-based adaptive approach for hybrid feature outlier detection. Fuzzy Sets Syst 421:1\u201328","journal-title":"Fuzzy Sets Syst"},{"issue":"8","key":"2470_CR41","doi-asserted-by":"publisher","first-page":"8399","DOI":"10.1109\/TCYB.2021.3058780","volume":"52","author":"Z Yuan","year":"2021","unstructured":"Yuan Z, Chen HM, Li TR, Liu J, Wang S (2021) Outlier detection based on fuzzy rough granules in mixed attribute data. IEEE Trans Cybern 52(8):8399\u20138412","journal-title":"IEEE Trans Cybern"},{"key":"2470_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2023.109087","volume":"165","author":"SH Wang","year":"2024","unstructured":"Wang SH, Yuan Z, Luo C, Chen HM, Peng DZ (2024) Exploiting fuzzy rough entropy to detect anomalies. Int J Approx Reason 165:109087","journal-title":"Int J Approx Reason"},{"issue":"9","key":"2470_CR43","doi-asserted-by":"publisher","first-page":"6338","DOI":"10.1016\/j.eswa.2010.02.087","volume":"37","author":"F Jiang","year":"2010","unstructured":"Jiang F, Sui Y, Cao C (2010) An information entropy-based approach to outlier detection in rough sets. Expert Syst Appl 37(9):6338\u20136344","journal-title":"Expert Syst Appl"},{"key":"2470_CR44","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.ins.2023.03.027","volume":"632","author":"P Wang","year":"2023","unstructured":"Wang P, He JL, Li ZW (2023) Attribute reduction for hybrid data based on fuzzy rough iterative computation model. Inf Sci 632:555\u2013575","journal-title":"Inf Sci"},{"key":"2470_CR45","doi-asserted-by":"publisher","first-page":"641","DOI":"10.1080\/03081070600687668","volume":"35","author":"JY Liang","year":"2006","unstructured":"Liang JY, Shi ZZ, Li DY, Wierman MJ (2006) The information entropy, rough entropy and knowledge granulation in incomplete information systems. Int J Gen Syst 35:641\u2013654","journal-title":"Int J Gen Syst"},{"key":"2470_CR46","doi-asserted-by":"crossref","unstructured":"Ramaswamy S, Rastogi R, Shim K (2000) Efficient algorithms for mining outliers from large data sets. In: Proceedings of the 2000 ACM Sigmod International Conference on Management of data, pp 427\u2013438","DOI":"10.1145\/342009.335437"},{"key":"2470_CR47","doi-asserted-by":"publisher","first-page":"4680","DOI":"10.1016\/j.eswa.2008.06.019","volume":"36","author":"F Jiang","year":"2009","unstructured":"Jiang F, Sui YF, Cao CG (2009) Some issues about outlier detection in rough set theory. Expert Syst Appl 36:4680\u20134687","journal-title":"Expert Syst Appl"},{"issue":"12","key":"2470_CR48","doi-asserted-by":"publisher","first-page":"8745","DOI":"10.1016\/j.eswa.2010.06.040","volume":"37","author":"YM Chen","year":"2010","unstructured":"Chen YM, Miao DQ, Zhang HY (2010) Neighborhood outlier detection. Expert Syst Appl 37(12):8745\u20138749","journal-title":"Expert Syst Appl"},{"key":"2470_CR49","doi-asserted-by":"crossref","unstructured":"Aggarwal CC, Yu PS (2001) Outlier detection for high dimensional data. In: Proceedings of the 2001 ACM Sigmod International Conference on Management of data, 2001, pp 37\u201346","DOI":"10.1145\/375663.375668"},{"issue":"4","key":"2470_CR50","doi-asserted-by":"publisher","first-page":"891","DOI":"10.1007\/s10618-015-0444-8","volume":"30","author":"GO Campos","year":"2016","unstructured":"Campos GO, Zimek A, Sander J, Campello RJ, Micenkov\u00e1 B, Schubert E, Assent I, Houle ME (2016) On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Min Knowl Disc 30(4):891\u2013927","journal-title":"Data Min Knowl Disc"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02470-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-024-02470-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-024-02470-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,7]],"date-time":"2025-06-07T09:02:37Z","timestamp":1749286957000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-024-02470-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,27]]},"references-count":50,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,6]]}},"alternative-id":["2470"],"URL":"https:\/\/doi.org\/10.1007\/s13042-024-02470-3","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"type":"print","value":"1868-8071"},{"type":"electronic","value":"1868-808X"}],"subject":[],"published":{"date-parts":[[2024,12,27]]},"assertion":[{"value":"6 June 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}