{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:11:14Z","timestamp":1772770274853,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"7-8","license":[{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T00:00:00Z","timestamp":1737504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172048"],"award-info":[{"award-number":["62172048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62172048"],"award-info":[{"award-number":["62172048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. & Cyber."],"published-print":{"date-parts":[[2025,8]]},"DOI":"10.1007\/s13042-025-02546-8","type":"journal-article","created":{"date-parts":[[2025,1,22]],"date-time":"2025-01-22T12:22:18Z","timestamp":1737548538000},"page":"4833-4847","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["The processing for label noise based on attribute reduction and two-step method"],"prefix":"10.1007","volume":"16","author":[{"given":"Xingyu","family":"Wu","sequence":"first","affiliation":[]},{"given":"Ping","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,22]]},"reference":[{"key":"2546_CR1","doi-asserted-by":"publisher","unstructured":"Benkendorf DJ, Schwartz SD, Cutler DR, Hawkins CP (2023) Correcting for the effects of class imbalance improves the performance of machine-learning based species distribution models. Ecol. Modell. 483:110414. https:\/\/doi.org\/10.1016\/j.ecolmodel.2023.110414","DOI":"10.1016\/j.ecolmodel.2023.110414"},{"key":"2546_CR2","doi-asserted-by":"publisher","unstructured":"Salkhordeh\u00a0Haghighi M, Javad Mashhadi\u00a0Hoseini M (2020) Improving machine learning accuracy in diagnosing diseases using feature selection based on the fruit- fly algorithm. In: 2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS), pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICSPIS51611.2020.9349593","DOI":"10.1109\/ICSPIS51611.2020.9349593"},{"key":"2546_CR3","doi-asserted-by":"publisher","unstructured":"Gong Y, Liu G, Xue Y, Li R, Meng L (2023) A survey on dataset quality in machine learning. Inf. Softw. Technol. 162:107268. https:\/\/doi.org\/10.1016\/j.infsof.2023.107268","DOI":"10.1016\/j.infsof.2023.107268"},{"key":"2546_CR4","unstructured":"Knorr EM, Ng RTY (1998) Algorithms for mining distance-based outliers in large datasets"},{"key":"2546_CR5","unstructured":"Goldstein M, Dengel A (2012) Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm. In: KI-2012: Poster and Demo Track"},{"key":"2546_CR6","doi-asserted-by":"publisher","unstructured":"Tang B, He H (2017) A local density-based approach for outlier detection. Neurocomputing 241:171\u2013180. https:\/\/doi.org\/10.1016\/j.neucom.2017.02.039","DOI":"10.1016\/j.neucom.2017.02.039"},{"issue":"3","key":"2546_CR7","doi-asserted-by":"publisher","first-page":"304","DOI":"10.1109\/TKDE.2006.46","volume":"18","author":"H Xiong","year":"2006","unstructured":"Xiong H, Pandey G, Steinbach M, Kumar V (2006) Enhancing data analysis with noise removal. IEEE Trans Knowl Data Eng 18(3):304\u2013319. https:\/\/doi.org\/10.1109\/TKDE.2006.46","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2546_CR8","doi-asserted-by":"crossref","unstructured":"Kriegel HP, Schubert M, Zimek A (2008) Angle-based outlier detection in high-dimensional data. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, August 24-27, 2008","DOI":"10.1145\/1401890.1401946"},{"key":"2546_CR9","doi-asserted-by":"publisher","unstructured":"Xia S, Xiong Z, Luo Y, Dong L, Xing C (2015) Relative density based support vector machine. Neurocomputing 149:1424\u20131432. https:\/\/doi.org\/10.1016\/j.neucom.2014.08.053","DOI":"10.1016\/j.neucom.2014.08.053"},{"key":"2546_CR10","doi-asserted-by":"publisher","unstructured":"Xia S, Huang L, Wang G, Gao X, Shao Y, Chen Z (2022) An adaptive and general model for label noise detection using relative probabilistic density. Knowl.-Based Syst. 239:107907. https:\/\/doi.org\/10.1016\/j.knosys.2021.107907","DOI":"10.1016\/j.knosys.2021.107907"},{"key":"2546_CR11","doi-asserted-by":"crossref","unstructured":"Liang X, Xia S, Liu Q, Liu Y, Wang G (2018) A multi-granular relative density model for class noise detection. IEEE","DOI":"10.1109\/BigData.2018.8622383"},{"key":"2546_CR12","doi-asserted-by":"publisher","unstructured":"Peng X, Wang P, Xia S, Wang C, Chen W (2022) Vpgb: A granular-ball based model for attribute reduction and classification with label noise. Inf. Sci. 611:504\u2013521. https:\/\/doi.org\/10.1016\/j.ins.2022.08.066","DOI":"10.1016\/j.ins.2022.08.066"},{"key":"2546_CR13","doi-asserted-by":"publisher","unstructured":"Chen Q, Jiang G, Cao F, Men C, Wang W (2024) A general elevating framework for label noise filters. Pattern Recognit. 147:110072. https:\/\/doi.org\/10.1016\/j.patcog.2023.110072","DOI":"10.1016\/j.patcog.2023.110072"},{"key":"2546_CR14","doi-asserted-by":"publisher","first-page":"4093","DOI":"10.1007\/s13042-024-02137-z","volume":"15","author":"C Li","year":"2024","unstructured":"Li C, Mao Z, Jia M (2024) A real-valued label noise cleaning method based on ensemble iterative filtering with noise score. Int J Mach Learn Cybern 15:4093\u20134118","journal-title":"Int J Mach Learn Cybern"},{"key":"2546_CR15","doi-asserted-by":"crossref","unstructured":"Dawid AP, Skene A (1979) Maximum likelihood estimation of observer error-rates using the em algorithm. Journal of The Royal Statistical Society Series C-applied Statistics 28:20\u201328","DOI":"10.2307\/2346806"},{"issue":"43","key":"2546_CR16","first-page":"1297","volume":"11","author":"VC Raykar","year":"2010","unstructured":"Raykar VC, Yu S, Zhao LH, Valadez GH, Florin C, Bogoni L, Moy L (2010) Learning from crowds. J Mach Learn Res 11(43):1297\u20131322","journal-title":"J Mach Learn Res"},{"key":"2546_CR17","doi-asserted-by":"crossref","unstructured":"Sheng VS, Provost FJ, Ipeirotis PG (2008) Get another label? improving data quality and data mining using multiple, noisy labelers. Organizations & Markets eJournal","DOI":"10.1145\/1401890.1401965"},{"key":"2546_CR18","doi-asserted-by":"publisher","unstructured":"Zhang J, Sheng V, Wu J, Wu X (2016) Multi-class ground truth inference in crowdsourcing with clustering. IEEE Trans. Knowl. Data Eng. 28:1080\u20131085. https:\/\/doi.org\/10.1109\/TKDE.2015.2504974 . Publisher Copyright: 2015 IEEE","DOI":"10.1109\/TKDE.2015.2504974"},{"key":"2546_CR19","doi-asserted-by":"publisher","unstructured":"Dong Y, Jiang L, Li C (2022) Improving data and model quality in crowdsourcing using co-training-based noise correction. Inf. Sci. 583:174\u2013188 https:\/\/doi.org\/10.1016\/j.ins.2021.11.021","DOI":"10.1016\/j.ins.2021.11.021"},{"key":"2546_CR20","doi-asserted-by":"crossref","unstructured":"Chen Z, Jiang L, Li C (2022) Label distribution-based noise correction for multiclass crowdsourcing. Int. J. Intell. Syst. 37:5752\u20135767","DOI":"10.1002\/int.22812"},{"key":"2546_CR21","doi-asserted-by":"publisher","unstructured":"Nicholson B, Sheng VS, Zhang J (2016) Label noise correction and application in crowdsourcing. Expert Syst. Appl. 66:149\u2013162. https:\/\/doi.org\/10.1016\/j.eswa.2016.09.003","DOI":"10.1016\/j.eswa.2016.09.003"},{"key":"2546_CR22","doi-asserted-by":"publisher","unstructured":"Wu X, Jiang L, Zhang W, Li C (2023) Three-way decision-based noise correction for crowdsourcing. Int. J. Approx. Reason. 160:108973. https:\/\/doi.org\/10.1016\/j.ijar.2023.108973","DOI":"10.1016\/j.ijar.2023.108973"},{"key":"2546_CR23","doi-asserted-by":"publisher","first-page":"3641","DOI":"10.1007\/s13042-023-01855-0","volume":"14","author":"K Zhu","year":"2023","unstructured":"Zhu K, Xue S, Jiang L (2023) Improving label quality in crowdsourcing using deep co-teaching-based noise correction. Int J Mach Learn Cybern 14:3641\u20133654","journal-title":"Int J Mach Learn Cybern"},{"key":"2546_CR24","doi-asserted-by":"publisher","unstructured":"Zhang Q, Song Y, Peng Y, Li Z (2024) Attribute reduction algorithms with an anti-noise mechanism for hybrid data based on fuzzy evidence theory. Eng. Appl. Artif. Intell. 129:107659. https:\/\/doi.org\/10.1016\/j.engappai.2023.107659","DOI":"10.1016\/j.engappai.2023.107659"},{"key":"2546_CR25","doi-asserted-by":"publisher","unstructured":"Yu B, Hu Y, Kang Y, Cai M (2023) A novel variable precision rough set attribute reduction algorithm based on local attribute significance. Int. J. Approx. Reason. 157:88\u2013104. https:\/\/doi.org\/10.1016\/j.ijar.2023.03.002","DOI":"10.1016\/j.ijar.2023.03.002"},{"issue":"7","key":"2546_CR26","doi-asserted-by":"publisher","first-page":"4027","DOI":"10.1109\/TFUZZ.2024.3387571","volume":"32","author":"C Wang","year":"2024","unstructured":"Wang C, Wang C, Qian Y, Leng Q (2024) Feature selection based on weighted fuzzy rough sets. IEEE Trans Fuzzy Syst 32(7):4027\u20134037. https:\/\/doi.org\/10.1109\/TFUZZ.2024.3387571","journal-title":"IEEE Trans Fuzzy Syst"},{"issue":"4","key":"2546_CR27","doi-asserted-by":"publisher","first-page":"2200","DOI":"10.1109\/TCYB.2021.3112674","volume":"53","author":"S An","year":"2023","unstructured":"An S, Zhao E, Wang C, Guo G, Zhao S, Li P (2023) Relative fuzzy rough approximations for feature selection and classification. IEEE Trans. Cybern. 53(4):2200\u20132210. https:\/\/doi.org\/10.1109\/TCYB.2021.3112674","journal-title":"IEEE Trans. Cybern."},{"key":"2546_CR28","doi-asserted-by":"crossref","unstructured":"Yao Y, Gao C Statistical interpretations of three-way decisions. In: Ciucci D, Wang G, Mitra S, Wu W-Z (eds.) Rough Sets and Knowledge Technology, pp. 309\u2013320. Springer, Cham (2015)","DOI":"10.1007\/978-3-319-25754-9_28"},{"issue":"23","key":"2546_CR29","doi-asserted-by":"publisher","first-page":"5169","DOI":"10.1016\/j.ins.2011.07.025","volume":"181","author":"D Chen","year":"2011","unstructured":"Chen D, Hu Q, Yang Y (2011) Parameterized attribute reduction with gaussian kernel based fuzzy rough sets. Inf Sci 181(23):5169\u20135179. https:\/\/doi.org\/10.1016\/j.ins.2011.07.025","journal-title":"Inf Sci"},{"issue":"5","key":"2546_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102064","volume":"36","author":"L Yin","year":"2024","unstructured":"Yin L, Cao K, Jiang Z, Li Z (2024) Ra-mrs: A high efficient attribute reduction algorithm in big data. Journal of King Saud University - Computer and Information Sciences 36(5):102064. https:\/\/doi.org\/10.1016\/j.jksuci.2024.102064","journal-title":"Journal of King Saud University - Computer and Information Sciences"},{"key":"2546_CR31","doi-asserted-by":"publisher","unstructured":"Dai J, Huang W, Wang W, Zhang C (2023) Semi-supervised attribute reduction based on label distribution and label irrelevance. Inf. Fusion 100:101951. https:\/\/doi.org\/10.1016\/j.inffus.2023.101951","DOI":"10.1016\/j.inffus.2023.101951"},{"key":"2546_CR32","doi-asserted-by":"publisher","unstructured":"Hu M, Guo Y, Wang R, Wang X (2024) Attribute reduction with fuzzy kernel-induced relations. Inf. Sci. 669:120589. https:\/\/doi.org\/10.1016\/j.ins.2024.120589","DOI":"10.1016\/j.ins.2024.120589"},{"key":"2546_CR33","doi-asserted-by":"publisher","unstructured":"Li Z, Huang H, Huang Q, Lin Y (2024) Attribute reduction for hybrid data based on statistical distribution of data and fuzzy evidence theory. Inf. Sci. 662:120247. https:\/\/doi.org\/10.1016\/j.ins.2024.120247","DOI":"10.1016\/j.ins.2024.120247"},{"key":"2546_CR34","doi-asserted-by":"crossref","unstructured":"Zhou Y, Yu H, Cai X (2010) A novel k-means algorithm for clustering and outlier detection. In: 2009 Second International Conference on Future Information Technology and Management Engineering","DOI":"10.1109\/FITME.2009.125"},{"issue":"3","key":"2546_CR35","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. https:\/\/doi.org\/10.1016\/j.ins.2009.09.021","journal-title":"Inf Sci"},{"issue":"6","key":"2546_CR36","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. https:\/\/doi.org\/10.1016\/j.ins.2010.11.019","journal-title":"Inf Sci"},{"key":"2546_CR37","doi-asserted-by":"publisher","unstructured":"Xia S, Wang G, Chen Z, Duan Y, liu Q (2019) Complete random forest based class noise filtering learning for improving the generalizability of classifiers. IEEE Trans. Knowl. Data Eng. 31(11):2063\u20132078. https:\/\/doi.org\/10.1109\/TKDE.2018.2873791","DOI":"10.1109\/TKDE.2018.2873791"},{"key":"2546_CR38","unstructured":"Xia S, Chen B, Wang G, Zheng Y, Chen Z (2021) mcrf and mrd: Two classification methods based on a novel multiclass label noise filtering learning framework. IEEE Trans. Neural Netw. Learn. Syst. PP(99):1\u201315"},{"issue":"7","key":"2546_CR39","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.1016\/S0167-8655(02)00225-8","volume":"24","author":"JS Sanchez","year":"2003","unstructured":"Sanchez JS, Barandela R, Marques AI, Alejo R, Badenas J (2003) Analysis of new techniques to obtain quality training sets. Pattern Recognit. Lett. 24(7):1015\u20131022","journal-title":"Pattern Recognit. Lett."},{"key":"2546_CR40","doi-asserted-by":"publisher","unstructured":"Ekambaram R, Fefilatyev S, Shreve M, Kramer K, Hall LO, Goldgof DB, Kasturi R (2016) Active cleaning of label noise. Pattern Recognit. 51:463\u2013480. https:\/\/doi.org\/10.1016\/j.patcog.2015.09.020","DOI":"10.1016\/j.patcog.2015.09.020"},{"issue":"1","key":"2546_CR41","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1214\/aoms\/1177731944","volume":"11","author":"M Friedman","year":"1940","unstructured":"Friedman M (1940) A comparison of alternative tests of significance for the problem of $$m$$ rankings. Ann Math Stat 11(1):86\u201392","journal-title":"Ann Math Stat"},{"issue":"1","key":"2546_CR42","first-page":"1","volume":"7","author":"J Demiar","year":"2006","unstructured":"Demiar J, Schuurmans D (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7(1):1\u201330","journal-title":"J Mach Learn Res"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02546-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-025-02546-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-025-02546-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T04:26:30Z","timestamp":1757132790000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-025-02546-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,22]]},"references-count":42,"journal-issue":{"issue":"7-8","published-print":{"date-parts":[[2025,8]]}},"alternative-id":["2546"],"URL":"https:\/\/doi.org\/10.1007\/s13042-025-02546-8","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-5122434\/v1","asserted-by":"object"}]},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,22]]},"assertion":[{"value":"20 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 January 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 January 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 no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Materials will be made available on request.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Materials availability"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}}]}}