{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T18:19:50Z","timestamp":1771957190709,"version":"3.50.1"},"reference-count":153,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T00:00:00Z","timestamp":1530835200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001807","name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","doi-asserted-by":"publisher","award":["2015\/20606-6"],"award-info":[{"award-number":["2015\/20606-6"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100006461","name":"Agencia de Innovaci\u00f3n y Desarrollo de Andaluc\u00eda","doi-asserted-by":"publisher","award":["P12-TIC-2958"],"award-info":[{"award-number":["P12-TIC-2958"]}],"id":[{"id":"10.13039\/501100006461","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003329","name":"Ministerio de Econom\u00eda y Competitividad","doi-asserted-by":"publisher","award":["TIN2014-57251-P"],"award-info":[{"award-number":["TIN2014-57251-P"]}],"id":[{"id":"10.13039\/501100003329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2019,7]]},"DOI":"10.1007\/s10115-018-1244-4","type":"journal-article","created":{"date-parts":[[2018,7,6]],"date-time":"2018-07-06T08:12:15Z","timestamp":1530864735000},"page":"63-97","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["Emerging topics and challenges of learning from noisy data in nonstandard classification: a survey beyond binary class noise"],"prefix":"10.1007","volume":"60","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8597-4987","authenticated-orcid":false,"given":"Ronaldo C.","family":"Prati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juli\u00e1n","family":"Luengo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,7,6]]},"reference":[{"key":"1244_CR1","doi-asserted-by":"crossref","unstructured":"Abell\u00e1n J, Masegosa AR (2010) Bagging decision trees on data sets with classification noise. In: International symposium on foundations of information and knowledge systems. Springer, pp 248\u2013265","DOI":"10.1007\/978-3-642-11829-6_17"},{"key":"1244_CR2","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.artint.2013.06.003","volume":"201","author":"J Amores","year":"2013","unstructured":"Amores J (2013) Multiple instance classification: review, taxonomy and comparative study. Artif Intell 201:81\u2013105","journal-title":"Artif Intell"},{"issue":"4","key":"1244_CR3","first-page":"343","volume":"2","author":"D Angluin","year":"1988","unstructured":"Angluin D, Laird P (1988) Learning from noisy examples. Mach Learn 2(4):343\u2013370","journal-title":"Mach Learn"},{"issue":"2","key":"1244_CR4","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10462-012-9374-7","volume":"43","author":"JA Baranauskas","year":"2015","unstructured":"Baranauskas JA (2015) The number of classes as a source for instability of decision tree algorithms in high dimensional datasets. Artif Intell Rev 43(2):301\u2013310","journal-title":"Artif Intell Rev"},{"issue":"473","key":"1244_CR5","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1198\/016214505000000907","volume":"101","author":"PL Bartlett","year":"2006","unstructured":"Bartlett PL, Jordan MI, McAuliffe JD (2006) Convexity, classification, and risk bounds. J Am Stat Assoc 101(473):138\u2013156","journal-title":"J Am Stat Assoc"},{"key":"1244_CR6","doi-asserted-by":"crossref","unstructured":"Beigman E, Klebanov BB (2009) Learning with annotation noise. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP: volume 1\u2013volume 1, ACL \u201909, pp 280\u2013287","DOI":"10.3115\/1687878.1687919"},{"issue":"3","key":"1244_CR7","doi-asserted-by":"crossref","first-page":"6627","DOI":"10.1016\/j.eswa.2008.08.021","volume":"36","author":"A Ben-David","year":"2009","unstructured":"Ben-David A, Sterling L, Tran T (2009) Adding monotonicity to learning algorithms may impair their accuracy. Expert Syst Appl 36(3):6627\u20136634","journal-title":"Expert Syst Appl"},{"issue":"7","key":"1244_CR8","doi-asserted-by":"crossref","first-page":"1622","DOI":"10.1016\/j.jmva.2010.03.001","volume":"101","author":"Y Bi","year":"2010","unstructured":"Bi Y, Jeske DR (2010) The efficiency of logistic regression compared to normal discriminant analysis under class-conditional classification noise. J Multivar Anal 101(7):1622\u20131637","journal-title":"J Multivar Anal"},{"issue":"5","key":"1244_CR9","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1007\/s00500-010-0657-0","volume":"15","author":"A Bouchachia","year":"2011","unstructured":"Bouchachia A (2011) Fuzzy classification in dynamic environments. Soft Comput 15(5):1009\u20131022","journal-title":"Soft Comput"},{"key":"1244_CR10","doi-asserted-by":"crossref","unstructured":"Brefeld U, Scheffer T (2004) Co-Em support vector learning. In: International conference on machine learning (ICML), p 16","DOI":"10.1145\/1015330.1015350"},{"key":"1244_CR11","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.neucom.2014.08.082","volume":"160","author":"FA Breve","year":"2015","unstructured":"Breve FA, Zhao L, Quiles MG (2015) Particle competition and cooperation for semi-supervised learning with label noise. Neurocomputing 160:63\u201372","journal-title":"Neurocomputing"},{"key":"1244_CR12","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1613\/jair.606","volume":"11","author":"CE Brodley","year":"1999","unstructured":"Brodley CE, Friedl MA (1999) Identifying mislabeled training data. J Artif Intell Res 11:131\u2013167","journal-title":"J Artif Intell Res"},{"issue":"1","key":"1244_CR13","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375","journal-title":"Mach Learn"},{"issue":"3","key":"1244_CR14","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1145\/1541880.1541882","volume":"41","author":"V Chandola","year":"2009","unstructured":"Chandola V, Banerjee A, Kumar V (2009) Anomaly detection: a survey. ACM Comput Surv 41(3):15","journal-title":"ACM Comput Surv"},{"key":"1244_CR15","doi-asserted-by":"crossref","unstructured":"Chapelle O, Shivaswamy P, Vadrevu S, Weinberger K, Zhang Y, Tseng B (2010) Multi-task learning for boosting with application to web search ranking. In: ACM SIGKDD international conference on knowledge discovery and data mining (KDD). ACM, pp 1189\u20131198","DOI":"10.1145\/1835804.1835953"},{"key":"1244_CR16","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.neucom.2014.08.091","volume":"163","author":"F Charte","year":"2015","unstructured":"Charte F, Rivera AJ, del Jes\u00fas MJ, Herrera F (2015) Addressing imbalance in multilabel classification: measures and random resampling algorithms. Neurocomputing 163:3\u201316","journal-title":"Neurocomputing"},{"key":"1244_CR17","unstructured":"Chen K, K\u00e4m\u00e4r\u00e4inen J-K (2016) Learning with ambiguous label distribution for apparent age estimation. In: Asian conference on computer vision. Springer, pp 330\u2013343"},{"issue":"1","key":"1244_CR18","doi-asserted-by":"crossref","first-page":"446","DOI":"10.32614\/RJ-2017-013","volume":"9","author":"P-Y Chen","year":"2017","unstructured":"Chen P-Y, Chen C-C, Yang C-H, Chang S-M, Lee K-J (2017) milr: Multiple-instance logistic regression with lasso penalty. R J 9(1):446\u2013457","journal-title":"R J"},{"key":"1244_CR19","unstructured":"Cheng W, H\u00fcllermeier E, Dembczynski KJ (2010) Bayes optimal multilabel classification via probabilistic classifier chains. In: International conference on machine learning (ICML), pp 279\u2013286"},{"issue":"1","key":"1244_CR20","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.patcog.2014.07.022","volume":"48","author":"V Cheplygina","year":"2015","unstructured":"Cheplygina V, Tax DM, Loog M (2015) Multiple instance learning with bag dissimilarities. Pattern Recognit 48(1):264\u2013275","journal-title":"Pattern Recognit"},{"key":"1244_CR21","unstructured":"Chevaleyre Y, Zucker J-D (2000) Noise-tolerant rule induction from multi-instance data. In: ICML 2000, workshop on attribute-value and relational learning"},{"issue":"5","key":"1244_CR22","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1109\/TSMCC.2005.855493","volume":"36","author":"HA Daniels","year":"2006","unstructured":"Daniels HA, Velikova MV (2006) Derivation of monotone decision models from noisy data. IEEE Trans Syst Man Cybern C 36(5):705\u2013710","journal-title":"IEEE Trans Syst Man Cybern C"},{"issue":"3","key":"1244_CR23","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1007\/s10618-015-0433-y","volume":"30","author":"ER Faria de","year":"2016","unstructured":"de Faria ER, de Leon Ferreira ACP, Gama J et al (2016) Minas: multiclass learning algorithm for novelty detection in data streams. Data Min Knowl Discov 30(3):640\u2013680","journal-title":"Data Min Knowl Discov"},{"issue":"1\u20132","key":"1244_CR24","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10994-012-5285-8","volume":"88","author":"K Dembczy\u0144ski","year":"2012","unstructured":"Dembczy\u0144ski K, Waegeman W, Cheng W, H\u00fcllermeier E (2012) On label dependence and loss minimization in multi-label classification. Mach Learn 88(1\u20132):5\u201345","journal-title":"Mach Learn"},{"key":"1244_CR25","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1613\/jair.105","volume":"2","author":"TG Dietterich","year":"1995","unstructured":"Dietterich TG, Bakiri G (1995) Solving multiclass learning problems via error-correcting output codes. J Artif Intell Res 2:263\u2013286","journal-title":"J Artif Intell Res"},{"issue":"4","key":"1244_CR26","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MCI.2015.2471196","volume":"10","author":"G Ditzler","year":"2015","unstructured":"Ditzler G, Roveri M, Alippi C, Polikar R (2015) Learning in nonstationary environments: a survey. IEEE Comput Intell Mag 10(4):12\u201325","journal-title":"IEEE Comput Intell Mag"},{"key":"1244_CR27","doi-asserted-by":"crossref","unstructured":"Du J, Cai Z (2015) Modelling class noise with symmetric and asymmetric distributions. In: AAAI conference on artificial intelligence (AAAI), pp 2589\u20132595","DOI":"10.1609\/aaai.v29i1.9612"},{"key":"1244_CR28","first-page":"615","volume":"6","author":"T Evgeniou","year":"2005","unstructured":"Evgeniou T, Micchelli CA, Pontil M (2005) Learning multiple tasks with kernel methods. J Mach Learn Res 6:615\u2013637","journal-title":"J Mach Learn Res"},{"key":"1244_CR29","doi-asserted-by":"crossref","unstructured":"Feelders A (2010) Monotone relabeling in ordinal classification. In: IEEE international conference on data mining (ICDM). IEEE, pp 803\u2013808","DOI":"10.1109\/ICDM.2010.92"},{"issue":"5","key":"1244_CR30","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TNNLS.2013.2292894","volume":"25","author":"B Fr\u00e9nay","year":"2014","unstructured":"Fr\u00e9nay B, Verleysen M (2014) Classification in the presence of label noise: a survey. IEEE Trans Neural Netw Learn Syst 25(5):845\u2013869","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"405","key":"1244_CR31","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1080\/01621459.1989.10478752","volume":"84","author":"JH Friedman","year":"1989","unstructured":"Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84(405):165\u2013175","journal-title":"J Am Stat Assoc"},{"issue":"7","key":"1244_CR32","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1287\/mnsc.38.7.913","volume":"38","author":"A Gaba","year":"1992","unstructured":"Gaba A, Winkler RL (1992) Implications of errors in survey data: a Bayesian model. Manag Sci 38(7):913\u2013925","journal-title":"Manag Sci"},{"issue":"2","key":"1244_CR33","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1002\/widm.1115","volume":"4","author":"MM Gaber","year":"2014","unstructured":"Gaber MM, Gama J, Krishnaswamy S, Gomes JB, Stahl F (2014) Data stream mining in ubiquitous environments: state-of-the-art and current directions. Wiley Interdiscip Rev Data Min Knowl Discov 4(2):116\u2013138","journal-title":"Wiley Interdiscip Rev Data Min Knowl Discov"},{"issue":"2","key":"1244_CR34","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1145\/1083784.1083789","volume":"34","author":"MM Gaber","year":"2005","unstructured":"Gaber MM, Zaslavsky A, Krishnaswamy S (2005) Mining data streams: a review. ACM Sigmod Record 34(2):18\u201326","journal-title":"ACM Sigmod Record"},{"issue":"10","key":"1244_CR35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v047.i10","volume":"47","author":"G Galimberti","year":"2012","unstructured":"Galimberti G, Soffritti G, Maso MD et al (2012) Classification trees for ordinal responses in r: the rpartscore package. J Stat Softw 47(10):1","journal-title":"J Stat Softw"},{"issue":"4","key":"1244_CR36","doi-asserted-by":"crossref","first-page":"44:1","DOI":"10.1145\/2523813","volume":"46","author":"J Gama","year":"2014","unstructured":"Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46(4):44:1\u201344:37","journal-title":"ACM Comput Surv"},{"key":"1244_CR37","unstructured":"Gamberger D, Boskovic R, Lavrac N, Groselj C (1999) Experiments with noise filtering in a medical domain. In: International conference on machine learning (ICML). Morgan Kaufmann Publishers, pp 143\u2013151"},{"key":"1244_CR38","doi-asserted-by":"crossref","unstructured":"Gamberger D, Lavra\u010d N, D\u017eeroski S (1996) Noise elimination in inductive concept learning: a case study in medical diagnosis. In: International workshop on algorithmic learning theory (ALT). Springer, pp 199\u2013212","DOI":"10.1007\/3-540-61863-5_47"},{"issue":"6","key":"1244_CR39","doi-asserted-by":"crossref","first-page":"2825","DOI":"10.1109\/TIP.2017.2689998","volume":"26","author":"B-B Gao","year":"2017","unstructured":"Gao B-B, Xing C, Xie C-W, Wu J, Geng X (2017) Deep label distribution learning with label ambiguity. IEEE Trans Image Process 26(6):2825\u20132838","journal-title":"IEEE Trans Image Process"},{"key":"1244_CR40","doi-asserted-by":"crossref","unstructured":"Gao J, Fan W, Han J (2007) On appropriate assumptions to mine data streams: analysis and practice. In: IEEE international conference on data mining (ICDM). IEEE, pp 143\u2013152","DOI":"10.1109\/ICDM.2007.96"},{"key":"1244_CR41","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-10247-4","volume-title":"Data preprocessing in data mining","author":"S Garc\u00eda","year":"2015","unstructured":"Garc\u00eda S, Luengo J, Herrera F (2015) Data preprocessing in data mining. Springer, Berlin"},{"key":"1244_CR42","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-28608-0","volume-title":"Data stream management: processing high-speed data streams","author":"M Garofalakis","year":"2016","unstructured":"Garofalakis M, Gehrke J, Rastogi R (2016) Data stream management: processing high-speed data streams. Springer, Berlin"},{"issue":"7","key":"1244_CR43","doi-asserted-by":"crossref","first-page":"1734","DOI":"10.1109\/TKDE.2016.2545658","volume":"28","author":"X Geng","year":"2016","unstructured":"Geng X (2016) Label distribution learning. IEEE Trans Knowl Data Eng 28(7):1734\u20131748","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1244_CR44","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.neucom.2014.09.081","volume":"160","author":"A Ghosh","year":"2015","unstructured":"Ghosh A, Manwani N, Sastry P (2015) Making risk minimization tolerant to label noise. Neurocomputing 160:93\u2013107","journal-title":"Neurocomputing"},{"issue":"3","key":"1244_CR45","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1145\/2716262","volume":"47","author":"E Gibaja","year":"2015","unstructured":"Gibaja E, Ventura S (2015) A tutorial on multilabel learning. ACM Comput Surv 47(3):52","journal-title":"ACM Comput Surv"},{"issue":"1","key":"1244_CR46","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1109\/TNNLS.2013.2271915","volume":"25","author":"JB Gomes","year":"2014","unstructured":"Gomes JB, Gaber MM, Sousa PA, Menasalvas E (2014) Mining recurring concepts in a dynamic feature space. IEEE Trans Neural Netw Learn Syst 25(1):95\u2013110","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"3","key":"1244_CR47","first-page":"171","volume":"5","author":"PA Guti\u00e9r rez","year":"2016","unstructured":"Guti\u00e9r rez PA, Garc\u00eda S (2016) Current prospects on ordinal and monotonic classification. Prog AI 5(3):171\u2013179","journal-title":"Prog AI"},{"issue":"1","key":"1244_CR48","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/TKDE.2015.2457911","volume":"28","author":"PA Guti\u00e9rrez","year":"2016","unstructured":"Guti\u00e9rrez PA, Perez-Ortiz M, Sanchez-Monedero J, Fern\u00e1ndez-Navarro F, Hervas-Martinez C (2016) Ordinal regression methods: survey and experimental study. IEEE Trans Knowl Data Eng 28(1):127\u2013146","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"8","key":"1244_CR49","doi-asserted-by":"crossref","first-page":"3846","DOI":"10.1109\/TIP.2017.2655445","volume":"26","author":"Z He","year":"2017","unstructured":"He Z, Li X, Zhang Z, Wu F, Geng X, Zhang Y, Yang M-H, Zhuang Y (2017) Data-dependent label distribution learning for age estimation. IEEE Trans Image Process 26(8):3846\u20133858","journal-title":"IEEE Trans Image Process"},{"key":"1244_CR50","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.patrec.2015.10.008","volume":"69","author":"J Hern\u00e1ndez-Gonz\u00e1lez","year":"2016","unstructured":"Hern\u00e1ndez-Gonz\u00e1lez J, Inza I, Lozano JA (2016) Weak supervision and other non-standard classification problems: a taxonomy. Pattern Recognit Lett 69:49\u201355","journal-title":"Pattern Recognit Lett"},{"key":"1244_CR51","volume-title":"Multilabel classification: problem analysis, metrics and techniques","author":"F Herrera","year":"2016","unstructured":"Herrera F, Charte F, Rivera AJ, del Jesus MJ (2016) Multilabel classification: problem analysis, metrics and techniques. Springer, Berlin"},{"key":"1244_CR52","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-47759-6","volume-title":"Multiple instance learning: foundations and algorithms","author":"F Herrera","year":"2016","unstructured":"Herrera F, Ventura S, Bello R, Cornelis C, Zafra A, S\u00e1nchez-Tarrag\u00f3 D, Vluymans S (2016) Multiple instance learning: foundations and algorithms. Springer, Berlin"},{"key":"1244_CR53","unstructured":"Hornung R (2017) Ordinal forests. Technical report 212. University of Munich, Department of Statistics"},{"issue":"11","key":"1244_CR54","doi-asserted-by":"crossref","first-page":"2052","DOI":"10.1109\/TKDE.2011.149","volume":"24","author":"Q Hu","year":"2012","unstructured":"Hu Q, Che X, Zhang L, Zhang D, Guo M, Yu D (2012) Rank entropy-based decision trees for monotonic classification. IEEE Trans Knowl Data Eng 24(11):2052\u20132064","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"1244_CR55","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1007\/s10618-013-0306-1","volume":"28","author":"PG Ipeirotis","year":"2014","unstructured":"Ipeirotis PG, Provost F, Sheng VS, Wang J (2014) Repeated labeling using multiple noisy labelers. Data Min Knowl Discov 28(2):402\u2013441","journal-title":"Data Min Knowl Discov"},{"key":"1244_CR56","doi-asserted-by":"crossref","unstructured":"Jabbari S, Holte RC, Zilles S (2012) Pac-learning with general class noise models. In: Annual conference on artificial intelligence. Springer, pp 73\u201384","DOI":"10.1007\/978-3-642-33347-7_7"},{"issue":"1","key":"1244_CR57","first-page":"4227","volume":"17","author":"J Josse","year":"2016","unstructured":"Josse J, Wager S (2016) Bootstrap-based regularization for low-rank matrix estimation. J Mach Learn Res 17(1):4227\u20134255","journal-title":"J Mach Learn Res"},{"key":"1244_CR58","first-page":"227","volume":"8","author":"R Khardon","year":"2007","unstructured":"Khardon R, Wachman G (2007) Noise tolerant variants of the perceptron algorithm. J Mach Learn Res 8:227\u2013248","journal-title":"J Mach Learn Res"},{"issue":"12","key":"1244_CR59","doi-asserted-by":"crossref","first-page":"3387","DOI":"10.1007\/s00500-014-1492-5","volume":"19","author":"B Krawczyk","year":"2015","unstructured":"Krawczyk B, Wo\u017aniak M (2015) One-class classifiers with incremental learning and forgetting for data streams with concept drift. Soft Comput 19(12):3387\u20133400","journal-title":"Soft Comput"},{"key":"1244_CR60","doi-asserted-by":"crossref","unstructured":"Kubat M (2015) Similarities: nearest neighbor classifiers. In: An introduction to machine learning. Springer, pp 43\u201364","DOI":"10.1007\/978-3-319-20010-1_3"},{"issue":"1","key":"1244_CR61","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1080\/00401706.1979.10489731","volume":"21","author":"PA Lachenbruch","year":"1979","unstructured":"Lachenbruch PA (1979) Note on initial misclassification effects on the quadratic discriminant function. Technometrics 21(1):129\u2013132","journal-title":"Technometrics"},{"key":"1244_CR62","unstructured":"Lawrence ND, Sch\u00f6lkopf B (2001) Estimating a kernel fisher discriminant in the presence of label noise. In: International conference on machine learning (ICML), pp 306\u2013313"},{"key":"1244_CR63","doi-asserted-by":"crossref","unstructured":"Leisch F, Weingessel A, Hornik K (1998) On the generation of correlated artificial binary data. SFB Adaptive information systems and modelling in economics and management science, 13. Working paper series, WU Vienna University of Economics and Business, Vienna","DOI":"10.32614\/CRAN.package.bindata"},{"key":"1244_CR64","doi-asserted-by":"crossref","unstructured":"Leung T, Song Y, Zhang J (2011) Handling label noise in video classification via multiple instance learning. In: IEEE international conference on computer vision (ICCV). IEEE, pp 2056\u20132063","DOI":"10.1109\/ICCV.2011.6126479"},{"issue":"5","key":"1244_CR65","doi-asserted-by":"crossref","first-page":"1713","DOI":"10.1109\/TFUZZ.2014.2374214","volume":"23","author":"S-T Li","year":"2015","unstructured":"Li S-T, Chen C-C (2015) A regularized monotonic fuzzy support vector machine model for data mining with prior knowledge. IEEE Trans Fuzzy Syst 23(5):1713\u20131727","journal-title":"IEEE Trans Fuzzy Syst"},{"key":"1244_CR66","doi-asserted-by":"crossref","unstructured":"Li W, Vasconcelos N (2015) Multiple instance learning for soft bags via top instances. In: IEEE conference on computer vision and pattern recognition (CVPR), pp 4277\u20134285","DOI":"10.1109\/CVPR.2015.7299056"},{"issue":"3","key":"1244_CR67","doi-asserted-by":"publisher","first-page":"865","DOI":"10.1016\/j.patcog.2012.08.018","volume":"46","author":"Y Li","year":"2013","unstructured":"Li Y, Tax DMJ, Duin RPW, Loog M (2013) Multiple-instance learning as a classifier combining problem. Pattern Recognit 46(3):865\u2013874. https:\/\/doi.org\/10.1016\/j.patcog.2012.08.018","journal-title":"Pattern Recognit"},{"issue":"5","key":"1244_CR68","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1162\/NECO_a_00265","volume":"24","author":"H-T Lin","year":"2012","unstructured":"Lin H-T, Li L (2012) Reduction from cost-sensitive ordinal ranking to weighted binary classification. Neural Comput 24(5):1329\u20131367","journal-title":"Neural Comput"},{"key":"1244_CR69","doi-asserted-by":"crossref","DOI":"10.1002\/9781119013563","volume-title":"Statistical analysis with missing data","author":"RJ Little","year":"2002","unstructured":"Little RJ, Rubin DB (2002) Statistical analysis with missing data. Wiley, New York"},{"key":"1244_CR70","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781139084789","volume-title":"Sentiment analysis: mining opinions, sentiments, and emotions","author":"B Liu","year":"2015","unstructured":"Liu B (2015) Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge University Press, Cambridge"},{"key":"1244_CR71","doi-asserted-by":"crossref","unstructured":"Lorena AC, Garcia L PF, de\u00a0Carvalho ACPLF (2015) Adapting noise filters for ranking. In: Brazilian conference on intelligent systems (BRACIS), pp 299\u2013304","DOI":"10.1109\/BRACIS.2015.58"},{"key":"1244_CR72","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.knosys.2017.10.026","volume":"140","author":"J Luengo","year":"2018","unstructured":"Luengo J, Shim S-O, Alshomrani S, Altalhi A, Herrera F (2018) CNC-NOS: class noise cleaning by ensemble filtering and noise scoring. Knowl Based Syst 140:27\u201349","journal-title":"Knowl Based Syst"},{"key":"1244_CR73","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.patcog.2015.10.014","volume":"52","author":"L Ma","year":"2016","unstructured":"Ma L, Destercke S, Wang Y (2016) Online active learning of decision trees with evidential data. Pattern Recognit 52:33\u201345","journal-title":"Pattern Recognit"},{"issue":"1","key":"1244_CR74","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1023\/A:1007661119649","volume":"41","author":"MA Maloof","year":"2000","unstructured":"Maloof MA, Michalski RS (2000) Selecting examples for partial memory learning. Mach Learn 41(1):27\u201352","journal-title":"Mach Learn"},{"issue":"3","key":"1244_CR75","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1109\/TSMCB.2012.2223460","volume":"43","author":"N Manwani","year":"2013","unstructured":"Manwani N, Sastry P (2013) Noise tolerance under risk minimization. IEEE Trans Cybern 43(3):1146\u20131151","journal-title":"IEEE Trans Cybern"},{"key":"1244_CR76","unstructured":"Maron O (1998) Learning from ambiguity. PhD thesis, Massachusetts Institute of Technology"},{"key":"1244_CR77","first-page":"570","volume":"10","author":"O Maron","year":"1998","unstructured":"Maron O, Lozano-P\u00e9rez T (1998) A framework for multiple-instance learning. Adv Neural Inf Process Syst 10:570\u2013576","journal-title":"Adv Neural Inf Process Syst"},{"issue":"6","key":"1244_CR78","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1109\/TKDE.2010.61","volume":"23","author":"M Masud","year":"2011","unstructured":"Masud M, Gao J, Khan L, Han J, Thuraisingham BM (2011) Classification and novel class detection in concept-drifting data streams under time constraints. IEEE Trans Knowl Data Eng 23(6):859\u2013874","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1244_CR79","doi-asserted-by":"crossref","unstructured":"Masud MM, Chen Q, Gao J, Khan L, Han J, Thuraisingham B (2010) Classification and novel class detection of data streams in a dynamic feature space. In: European conference on machine learning and principles and practice of knowledge discovery (ECML\/PKDD). Springer, pp 337\u2013352","DOI":"10.1007\/978-3-642-15883-4_22"},{"issue":"7","key":"1244_CR80","doi-asserted-by":"crossref","first-page":"1484","DOI":"10.1109\/TKDE.2012.109","volume":"25","author":"MM Masud","year":"2013","unstructured":"Masud MM, Chen Q, Khan L, Aggarwal CC, Gao J, Han J, Srivastava A, Oza NC (2013) Classification and adaptive novel class detection of feature-evolving data streams. IEEE Trans Knowl Data Eng 25(7):1484\u20131497","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"2","key":"1244_CR81","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1080\/00401706.1972.10488926","volume":"14","author":"G McLachlan","year":"1972","unstructured":"McLachlan G (1972) Asymptotic results for discriminant analysis when the initial samples are misclassified. Technometrics 14(2):415\u2013422","journal-title":"Technometrics"},{"issue":"11","key":"1244_CR82","doi-asserted-by":"crossref","first-page":"2216","DOI":"10.1109\/TNNLS.2015.2475750","volume":"27","author":"Q Miao","year":"2016","unstructured":"Miao Q, Cao Y, Xia G, Gong M, Liu J, Song J (2016) Rboost: label noise-robust boosting algorithm based on a nonconvex loss function and the numerically stable base learners. IEEE Trans Neural Netw Learn Syst 27(11):2216\u20132228","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"371","key":"1244_CR83","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1080\/01621459.1980.10477539","volume":"75","author":"JE Michalek","year":"1980","unstructured":"Michalek JE, Tripathi RC (1980) The effect of errors in diagnosis and measurement on the estimation of the probability of an event. J Am Stat Assoc 75(371):713\u2013721","journal-title":"J Am Stat Assoc"},{"issue":"1","key":"1244_CR84","first-page":"p30","volume":"3","author":"I Milstein","year":"2013","unstructured":"Milstein I, David AB, Potharst R (2013) Generating noisy monotone ordinal datasets. Artif Intell Rev 3(1):p30","journal-title":"Artif Intell Rev"},{"issue":"5","key":"1244_CR85","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1109\/TKDE.2009.156","volume":"22","author":"LL Minku","year":"2010","unstructured":"Minku LL, White AP, Yao X (2010) The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans Knowl Data Eng 22(5):730\u2013742","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1244_CR86","first-page":"424","volume-title":"Proceedings of the hybrid artificial intelligence systems: 4th international conference, HAIS 2009, Salamanca, Spain","author":"ALB Miranda","year":"2009","unstructured":"Miranda ALB, Garcia LPF, Carvalho ACPLF, Lorena AC (2009) Use of classification algorithms in noise detection and elimination. In: Corchado E, Wu X, Oja E, Herrero \u00c1, Baruque B (eds) Proceedings of the hybrid artificial intelligence systems: 4th international conference, HAIS 2009, Salamanca, Spain. Springer, Berlin, pp 424\u2013471"},{"issue":"3","key":"1244_CR87","doi-asserted-by":"crossref","first-page":"1494","DOI":"10.1016\/j.patcog.2013.09.029","volume":"47","author":"E Monta\u00f1es","year":"2014","unstructured":"Monta\u00f1es E, Senge R, Barranquero J, Quevedo JR, del Coz JJ, H\u00fcllermeier E (2014) Dependent binary relevance models for multi-label classification. Pattern Recognit 47(3):1494\u20131508","journal-title":"Pattern Recognit"},{"key":"1244_CR88","doi-asserted-by":"crossref","unstructured":"Napiera\u0142a K, Stefanowski J, Wilk S (2010) Learning from imbalanced data in presence of noisy and borderline examples. In: International conference on rough sets and current trends in computing. Springer, pp 158\u2013167","DOI":"10.1007\/978-3-642-13529-3_18"},{"key":"1244_CR89","unstructured":"Natarajan N, Dhillon IS, Ravikumar PK, Tewari A (2013) Learning with noisy labels. In: Advances in neural information processing systems (NIPS), pp 1196\u20131204"},{"issue":"4","key":"1244_CR90","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10462-010-9156-z","volume":"33","author":"DF Nettleton","year":"2010","unstructured":"Nettleton DF, Orriols-Puig A, Fornells A (2010) A study of the effect of different types of noise on the precision of supervised learning techniques. Artif Intell Rev 33(4):275\u2013306","journal-title":"Artif Intell Rev"},{"key":"1244_CR91","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.eswa.2016.09.003","volume":"66","author":"B Nicholson","year":"2016","unstructured":"Nicholson B, Sheng VS, Zhang J (2016) Label noise correction and application in crowdsourcing. Expert Syst Appl 66:149\u2013162","journal-title":"Expert Syst Appl"},{"key":"1244_CR92","doi-asserted-by":"crossref","unstructured":"Nowak S, R\u00fcger S (2010) How reliable are annotations via crowdsourcing: a study about inter-annotator agreement for multi-label image annotation. In: International conference on multimedia information retrieval (ICMR). ACM, pp 557\u2013566","DOI":"10.1145\/1743384.1743478"},{"issue":"1","key":"1244_CR93","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/S0304-3975(02)00424-3","volume":"298","author":"S Okamoto","year":"2003","unstructured":"Okamoto S, Yugami N (2003) Effects of domain characteristics on instance-based learning algorithms. Theor Comput Sci 298(1):207\u2013233","journal-title":"Theor Comput Sci"},{"issue":"3","key":"1244_CR94","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TPAMI.2009.23","volume":"32","author":"M Ozuysal","year":"2010","unstructured":"Ozuysal M, Calonder M, Lepetit V, Fua P (2010) Fast keypoint recognition using random ferns. IEEE Trans Pattern Anal Mach Intell 32(3):448\u2013461","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"10","key":"1244_CR95","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1244_CR96","unstructured":"Pathak D, Shelhamer E, Long J, Darrell T (2015) Fully convolutional multi-class multiple instance learning. In: International conference on learning representations (ICLR) workshop. arXiv:1412.7144"},{"key":"1244_CR97","volume-title":"Probabilistic reasoning in intelligent systems: networks of plausible inference","author":"J Pearl","year":"1988","unstructured":"Pearl J (1988) Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, Burlington"},{"issue":"1","key":"1244_CR98","first-page":"71","volume":"101","author":"CJ P\u00e9rez","year":"2007","unstructured":"P\u00e9rez CJ, Gonz\u00e1lez-Torre FJG, Mart\u00edn J, Ruiz M, Rojano C (2007) Misclassified multinomial data: a Bayesian approach. RACSAM 101(1):71\u201380","journal-title":"RACSAM"},{"key":"1244_CR99","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.knosys.2015.10.011","volume":"92","author":"PS Perez","year":"2016","unstructured":"Perez PS, Nozawa SR, Macedo AA, Baranauskas JA (2016) Windowing improvements towards more comprehensible models. Knowl Based Syst 92:9\u201322","journal-title":"Knowl Based Syst"},{"issue":"1","key":"1244_CR100","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1007\/s10115-014-0794-3","volume":"45","author":"RC Prati","year":"2015","unstructured":"Prati RC, Batista GEAPA, Silva DF (2015) Class imbalance revisited: a new experimental setup to assess the performance of treatment methods. Knowl Inf Syst 45(1):247\u2013270","journal-title":"Knowl Inf Syst"},{"key":"1244_CR101","doi-asserted-by":"crossref","unstructured":"Qi Z, Yang M, Zhang ZM, Zhang Z (2012) Mining noisy tagging from multi-label space. In: ACM international conference on information and knowledge management (CIKM). ACM, pp 1925\u20131929","DOI":"10.1145\/2396761.2398545"},{"key":"1244_CR102","doi-asserted-by":"crossref","unstructured":"Qu W, Zhang Y, Zhu J, Qiu Q (2009) Mining multi-label concept-drifting data streams using dynamic classifier ensemble. In: Asian conference on machine learning (ACML). Springer, pp 308\u2013321","DOI":"10.1007\/978-3-642-05224-8_24"},{"issue":"1","key":"1244_CR103","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1(1):81\u2013106","journal-title":"Mach Learn"},{"key":"1244_CR104","volume-title":"C4. 5: programs for machine learning","author":"JR Quinlan","year":"1993","unstructured":"Quinlan JR (1993) C4. 5: programs for machine learning. Elsevier, New York"},{"issue":"1","key":"1244_CR105","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1080\/10556788.2010.507272","volume":"27","author":"M Rademaker","year":"2012","unstructured":"Rademaker M, De Baets B, De Meyer H (2012) Optimal monotone relabelling of partially non-monotone ordinal data. Optim Methods Softw 27(1):17\u201331","journal-title":"Optim Methods Softw"},{"key":"1244_CR106","unstructured":"Rakitsch B, Lippert C, Borgwardt K, Stegle O (2013) It is all in the noise: efficient multi-task Gaussian process inference with structured residuals. In: Advances in neural information processing systems (NIPS), pp 1466\u20131474"},{"key":"1244_CR107","doi-asserted-by":"crossref","unstructured":"Ralaivola L, Denis F, Magnan CN (2006) CN = CPCN. In: Proceedings of the 23rd international conference on Machine learning. ACM, pp 721\u2013728","DOI":"10.1145\/1143844.1143935"},{"issue":"3","key":"1244_CR108","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1007\/s10994-011-5256-5","volume":"85","author":"J Read","year":"2011","unstructured":"Read J, Pfahringer B, Holmes G, Frank E (2011) Classifier chains for multi-label classification. Mach Learn 85(3):333\u2013359","journal-title":"Mach Learn"},{"key":"1244_CR109","doi-asserted-by":"crossref","unstructured":"Rider AK, Johnson RA, Davis DA, Hoens TR, Chawla NV (2013) Classifier evaluation with missing negative class labels. In: International symposium on intelligent data analysis. Springer, pp\u00a0380\u2013391","DOI":"10.1007\/978-3-642-41398-8_33"},{"key":"1244_CR110","unstructured":"Rolnick D, Veit A, Belongie S, Shavit N (2017) Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694"},{"key":"1244_CR111","doi-asserted-by":"crossref","first-page":"2374","DOI":"10.1016\/j.neucom.2017.11.012","volume":"275","author":"M Sabzevari","year":"2018","unstructured":"Sabzevari M, Mart\u00ednez-Mu\u00f1oz G, Su\u00e1rez A (2018) A two-stage ensemble method for the detection of class-label noise. Neurocomputing 275:2374\u20132383","journal-title":"Neurocomputing"},{"issue":"1","key":"1244_CR112","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s10115-012-0570-1","volume":"38","author":"JA S\u00e1ez","year":"2014","unstructured":"S\u00e1ez JA, Galar M, Luengo J, Herrera F (2014) Analyzing the presence of noise in multi-class problems: alleviating its influence with the one-vs-one decomposition. Knowl Inf Syst 38(1):179\u2013206","journal-title":"Knowl Inf Syst"},{"key":"1244_CR113","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.inffus.2015.04.002","volume":"27","author":"JA S\u00e1ez","year":"2016","unstructured":"S\u00e1ez JA, Galar M, Luengo J, Herrera F (2016) INFFC: an iterative class noise filter based on the fusion of classifiers with noise sensitivity control. Inform Fusion 27:19\u201332","journal-title":"Inform Fusion"},{"key":"1244_CR114","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.ins.2014.08.051","volume":"291","author":"JA S\u00e1ez","year":"2015","unstructured":"S\u00e1ez JA, Luengo J, Stefanowski J, Herrera F (2015) Smote-ipf: addressing the noisy and borderline examples problem in imbalanced classification by a re-sampling method with filtering. Inf Sci 291:184\u2013203","journal-title":"Inf Sci"},{"issue":"6","key":"1244_CR115","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/S0167-8655(97)00035-4","volume":"18","author":"JS S\u00e1nchez","year":"1997","unstructured":"S\u00e1nchez JS, Pla F, Ferri FJ (1997) Prototype selection for the nearest neighbour rule through proximity graphs. Pattern Recognit Lett 18(6):507\u2013513","journal-title":"Pattern Recognit Lett"},{"key":"1244_CR116","unstructured":"Scott C (2015) A rate of convergence for mixture proportion estimation, with application to learning from noisy labels. In: International conference on artificial intelligence and statistics (AISTATS), pp\u00a0838\u2013846"},{"issue":"2","key":"1244_CR117","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10618-012-0299-1","volume":"28","author":"B Sluban","year":"2014","unstructured":"Sluban B, Gamberger D, Lavra\u010d N (2014) Ensemble-based noise detection: noise ranking and visual performance evaluation. Data Min Knowl Discov 28(2):265\u2013303","journal-title":"Data Min Knowl Discov"},{"key":"1244_CR118","unstructured":"Street WN, Kim Y (2001) A streaming ensemble algorithm (sea) for large-scale classification. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, pp\u00a0377\u2013382"},{"key":"1244_CR119","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.knosys.2016.05.035","volume":"108","author":"E Sulis","year":"2016","unstructured":"Sulis E, Far\u00edas DIH, Rosso P, Patti V, Ruffo G (2016) Figurative messages and affect in twitter: differences between #irony, #sarcasm and #not. Knowl Based Syst 108:132\u2013143","journal-title":"Knowl Based Syst"},{"key":"1244_CR120","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.knosys.2016.03.024","volume":"102","author":"B Sun","year":"2016","unstructured":"Sun B, Chen S, Wang J, Chen H (2016) A robust multi-class AdaBoost algorithm for mislabeled noisy data. Knowl Based Syst 102:87\u2013102","journal-title":"Knowl Based Syst"},{"issue":"7\u20138","key":"1244_CR121","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1007\/s00521-013-1362-6","volume":"23","author":"S Sun","year":"2013","unstructured":"Sun S (2013) A survey of multi-view machine learning. Neural Comput Appl 23(7\u20138):2031\u20132038","journal-title":"Neural Comput Appl"},{"issue":"6","key":"1244_CR122","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1109\/TKDE.2016.2526675","volume":"28","author":"Y Sun","year":"2016","unstructured":"Sun Y, Tang K, Minku LL, Wang S, Yao X (2016) Online ensemble learning of data streams with gradually evolved classes. IEEE Trans Knowl Data Eng 28(6):1532\u20131545","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1244_CR123","doi-asserted-by":"crossref","unstructured":"Tan M, Shi Q, van\u00a0den Hengel A, Shen C, Gao J, Hu F, Zhang Z (2015) Learning graph structure for multi-label image classification via clique generation. In: IEEE conference on computer vision and pattern recognition (CVPR), pp\u00a04100\u20134109","DOI":"10.1109\/CVPR.2015.7299037"},{"key":"1244_CR124","unstructured":"Teng C-M (1999) Correcting noisy data. In: Proceedings of the sixteenth international conference on machine learning. Morgan Kaufmann Publishers, San Francisco, CA, USA, pp\u00a0239\u2013248"},{"key":"1244_CR125","unstructured":"Tu H-H, Lin H-T (2010) One-sided support vector regression for multiclass cost-sensitive classification. In: International conference on machine learning (ICML), pp\u00a01095\u20131102"},{"issue":"12","key":"1244_CR126","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/j.datak.2009.08.005","volume":"68","author":"J Hulse Van","year":"2009","unstructured":"Van Hulse J, Khoshgoftaar T (2009) Knowledge discovery from imbalanced and noisy data. Data Knowl Eng 68(12):1513\u20131542","journal-title":"Data Knowl Eng"},{"issue":"2","key":"1244_CR127","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s10994-008-5077-3","volume":"73","author":"C Vens","year":"2008","unstructured":"Vens C, Struyf J, Schietgat L, D\u017eeroski S, Blockeel H (2008) Decision trees for hierarchical multi-label classification. Mach Learn 73(2):185\u2013214","journal-title":"Mach Learn"},{"issue":"4","key":"1244_CR128","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1109\/TSMCB.2012.2187280","volume":"42","author":"S Wang","year":"2012","unstructured":"Wang S, Yao X (2012) Multiclass imbalance problems: analysis and potential solutions. IEEE Trans Syst Man Cybern B 42(4):1119\u20131130","journal-title":"IEEE Trans Syst Man Cybern B"},{"key":"1244_CR129","doi-asserted-by":"crossref","unstructured":"Wei Y, Zheng Y, Yang Q (2016) Transfer knowledge between cities. In: ACM SIGKDD conference on knowledge discovery and data mining (KDD). ACM, pp\u00a01905\u20131914","DOI":"10.1145\/2939672.2939830"},{"key":"1244_CR130","unstructured":"Xiao H, Xiao H, Eckert C (2012) Adversarial label flips attack on support vector machines. In: Proceedings of the 20th european conference on artificial intelligence. IOS Press, pp\u00a0870\u2013875"},{"key":"1244_CR131","unstructured":"Xiao T, Xia T, Yang Y, Huang C, Wang X (2015) Learning from massive noisy labeled data for image classification. In: IEEE conference on computer vision and pattern recognition (CVPR), pp\u00a02691\u20132699"},{"key":"1244_CR132","doi-asserted-by":"crossref","unstructured":"Xing C, Geng X, Xue H (2016) Logistic boosting regression for label distribution learning, In: \u2018Proceedings of the IEEE conference on computer vision and pattern recognition, pp\u00a04489\u20134497","DOI":"10.1109\/CVPR.2016.486"},{"issue":"4","key":"1244_CR133","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.dss.2010.08.021","volume":"50","author":"K Xu","year":"2011","unstructured":"Xu K, Liao SS, Li J, Song Y (2011) Mining comparative opinions from customer reviews for competitive intelligence. Decis Support Syst 50(4):743\u2013754","journal-title":"Decis Support Syst"},{"key":"1244_CR134","doi-asserted-by":"crossref","unstructured":"Xu L, Wang Z, Shen Z, Wang Y, Chen E (2014) Learning low-rank label correlations for multi-label classification with missing labels. In: International conference on data mining (ICDM). IEEE, pp 1067\u20131072","DOI":"10.1109\/ICDM.2014.125"},{"key":"1244_CR135","doi-asserted-by":"crossref","unstructured":"Xu M, Zhou Z-H (2017) Incomplete label distribution learning. In: Proceedings of the 26th international joint conference on artificial intelligence. AAAI Press, pp\u00a03175\u20133181","DOI":"10.24963\/ijcai.2017\/443"},{"issue":"03","key":"1244_CR136","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1142\/S021946780700274X","volume":"7","author":"X Xu","year":"2007","unstructured":"Xu X, Li B (2007) Multiple class multiple-instance learning and its application to image categorization. Int J Image Graph 7(03):427\u2013444","journal-title":"Int J Image Graph"},{"key":"1244_CR137","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1016\/j.neucom.2015.03.046","volume":"162","author":"C-Y Yang","year":"2015","unstructured":"Yang C-Y, Wang J-J, Chou J-J, Lian F-L (2015) Confirming robustness of fuzzy support vector machine via $$\\xi $$ \u03be - $$\\alpha $$ \u03b1 bound. Neurocomputing 162:256\u2013266","journal-title":"Neurocomputing"},{"key":"1244_CR138","unstructured":"Yogatama D, Mann G (2014) Efficient transfer learning method for automatic hyperparameter tuning, In: Artificial intelligence and statistics, pp\u00a01077\u20131085"},{"issue":"10","key":"1244_CR139","doi-asserted-by":"crossref","first-page":"4349","DOI":"10.1109\/TIP.2012.2205006","volume":"21","author":"X-T Yuan","year":"2012","unstructured":"Yuan X-T, Liu X, Yan S (2012) Visual classification with multitask joint sparse representation. IEEE Trans Image Process 21(10):4349\u20134360","journal-title":"IEEE Trans Image Process"},{"issue":"4","key":"1244_CR140","first-page":"331","volume":"17","author":"X Zeng","year":"2008","unstructured":"Zeng X, Martinez T (2008) Using decision trees and soft labeling to filter mislabeled data. J Intell Syst 17(4):331\u2013354","journal-title":"J Intell Syst"},{"issue":"20","key":"1244_CR141","doi-asserted-by":"crossref","first-page":"2708","DOI":"10.1093\/bioinformatics\/btp478","volume":"25","author":"C Zhang","year":"2009","unstructured":"Zhang C, Wu C, Blanzieri E, Zhou Y, Wang Y, Du W, Liang Y (2009) Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model. Bioinformatics 25(20):2708\u20132714","journal-title":"Bioinformatics"},{"issue":"2","key":"1244_CR142","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.dss.2010.11.004","volume":"50","author":"P Zhang","year":"2011","unstructured":"Zhang P, Zhu X, Shi Y, Guo L, Wu X (2011) Robust ensemble learning for mining noisy data streams. Decis Support Syst 50(2):469\u2013479","journal-title":"Decis Support Syst"},{"issue":"3","key":"1244_CR143","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1093\/bioinformatics\/bti738","volume":"22","author":"W Zhang","year":"2006","unstructured":"Zhang W, Rekaya R, Bertrand K (2006) A method for predicting disease subtypes in presence of misclassification among training samples using gene expression: application to human breast cancer. Bioinformatics 22(3):317\u2013325","journal-title":"Bioinformatics"},{"issue":"9","key":"1244_CR144","doi-asserted-by":"crossref","first-page":"3151","DOI":"10.1016\/j.patcog.2010.03.021","volume":"43","author":"Z Zhang","year":"2010","unstructured":"Zhang Z, Zhou J (2010) Transfer estimation of evolving class priors in data stream classification. Pattern Recognit 43(9):3151\u20133161","journal-title":"Pattern Recognit"},{"key":"1244_CR145","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.neuroimage.2013.03.073","volume":"78","author":"J Zhou","year":"2013","unstructured":"Zhou J, Liu J, Narayan VA, Ye J, Initiative ADN et al (2013) Modeling disease progression via multi-task learning. Neuroimage 78:233\u2013248","journal-title":"Neuroimage"},{"issue":"1","key":"1244_CR146","doi-asserted-by":"crossref","first-page":"2291","DOI":"10.1016\/j.artint.2011.10.002","volume":"176","author":"Z-H Zhou","year":"2012","unstructured":"Zhou Z-H, Zhang M-L, Huang S-J, Li Y-F (2012) Multi-instance multi-label learning. Artif Intell 176(1):2291\u20132320","journal-title":"Artif Intell"},{"issue":"3","key":"1244_CR147","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/s10462-004-0751-8","volume":"22","author":"X Zhu","year":"2004","unstructured":"Zhu X, Wu X (2004a) Class noise vs. attribute noise: a quantitative study. Artif Intell Rev 22(3):177\u2013210","journal-title":"Artif Intell Rev"},{"key":"1244_CR148","unstructured":"Zhu X, Wu X (2004b) Cost-guided class noise handling for effective cost-sensitive learning. In: IEEE international conference on data mining (ICDM), IEEE, pp\u00a0297\u2013304"},{"key":"1244_CR149","unstructured":"Zhu X, Wu X, Chen Q (2003) Eliminating class noise in large datasets. In: International conference on machine learning (ICML), vol\u00a03, pp\u00a0920\u2013927"},{"issue":"2\u20133","key":"1244_CR150","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1007\/s10618-005-0012-8","volume":"12","author":"X Zhu","year":"2006","unstructured":"Zhu X, Wu X, Chen Q (2006) Bridging local and global data cleansing: Identifying class noise in large, distributed data datasets. Data Min Knowl Discov 12(2\u20133):275\u2013308","journal-title":"Data Min Knowl Discov"},{"key":"1244_CR151","unstructured":"Zhu X, Wu X, Khoshgoftaar TM, Shi Y (2007) An empirical study of the noise impact on cost-sensitive learning. In: International joint conference on artificial intelligence (IJCAI), vol 7, pp 1168\u20131173"},{"key":"1244_CR152","doi-asserted-by":"crossref","unstructured":"Zhu Y, Shasha D (2002) Statstream: statistical monitoring of thousands of data streams in real time. In: International conference on very large data bases (VLDB), VLDB Endowment, pp\u00a0358\u2013369","DOI":"10.1016\/B978-155860869-6\/50039-1"},{"issue":"1","key":"1244_CR153","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TNNLS.2012.2236570","volume":"25","author":"I \u017dliobait\u0117","year":"2014","unstructured":"\u017dliobait\u0117 I, Bifet A, Pfahringer B, Holmes G (2014) Active learning with drifting streaming data. IEEE Trans Neural Netw Learn Syst 25(1):27\u201339","journal-title":"IEEE Trans Neural Netw Learn Syst"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-018-1244-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-018-1244-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-018-1244-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,8]],"date-time":"2024-07-08T00:01:58Z","timestamp":1720396918000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-018-1244-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,6]]},"references-count":153,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,7]]}},"alternative-id":["1244"],"URL":"https:\/\/doi.org\/10.1007\/s10115-018-1244-4","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,6]]},"assertion":[{"value":"17 February 2017","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2018","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2018","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2018","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}