{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T07:48:51Z","timestamp":1772524131491,"version":"3.50.1"},"reference-count":49,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2016,7,13]],"date-time":"2016-07-13T00:00:00Z","timestamp":1468368000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001807","name":"FAPESP","doi-asserted-by":"crossref","award":["2011\/14602-7"],"award-info":[{"award-number":["2011\/14602-7"]}],"id":[{"id":"10.13039\/501100001807","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2016,9]]},"DOI":"10.1007\/s10618-016-0475-9","type":"journal-article","created":{"date-parts":[[2016,7,13]],"date-time":"2016-07-13T03:42:02Z","timestamp":1468381322000},"page":"1192-1216","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Ensembles of label noise filters: a ranking approach"],"prefix":"10.1007","volume":"30","author":[{"given":"Lu\u00eds P. F.","family":"Garcia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ana C.","family":"Lorena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stan","family":"Matwin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9 C. P. L. F.","family":"de Carvalho","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,7,13]]},"reference":[{"key":"475_CR1","unstructured":"Bensusan H, Giraud-Carrier CG, Kennedy CJ (2000) A higher-order approach to meta-learning. In: 10th international conference on inductive logic programming, pp 33\u201342"},{"key":"475_CR2","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-540-73263-1","volume-title":"Metalearning\u2014applications to data mining. Cognitive technologies","author":"P Brazdil","year":"2009","unstructured":"Brazdil P, Giraud-Carrier CG, Soares C, Vilalta R (2009) Metalearning\u2014applications to data mining. Cognitive technologies. Springer, Berlin"},{"issue":"1","key":"475_CR3","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45(1):5\u201332","journal-title":"Mach Learn"},{"key":"475_CR4","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"},{"key":"475_CR5","first-page":"312","volume-title":"Encyclopedia of machine learning","author":"G Brown","year":"2010","unstructured":"Brown G (2010) Ensemble learning. Encyclopedia of machine learning. Springer, Berlin, pp 312\u2013320"},{"key":"475_CR6","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"key":"475_CR7","volume-title":"Cluster analysis","author":"BS Everitt","year":"2009","unstructured":"Everitt BS, Landau S, Leese M (2009) Cluster analysis. Wiley, New York"},{"issue":"5","key":"475_CR8","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TNNLS.2013.2292894","volume":"25","author":"B Frenay","year":"2014","unstructured":"Frenay 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"},{"key":"475_CR9","unstructured":"Gamberger D, Lavra\u010d N, Groselj C (1999) Experiments with noise filtering in a medical domain. In: 16th international conference on machine learning (ICML), pp 143\u2013151"},{"key":"475_CR10","doi-asserted-by":"crossref","unstructured":"Garcia LPF, Lorena AC, de\u00a0Carvalho ACPLF (2012) A study on class noise detection and elimination. In: Brazilian symposium on neural networks (SBRN), pp 13\u201318","DOI":"10.1109\/SBRN.2012.49"},{"key":"475_CR11","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.neucom.2014.10.085","volume":"160","author":"LPF Garcia","year":"2015","unstructured":"Garcia LPF, de Carvalho ACPLF, Lorena AC (2015a) Effect of label noise in the complexity of classification problems. Neurocomputing 160:108\u2013119","journal-title":"Neurocomputing"},{"key":"475_CR12","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.knosys.2015.09.023","volume":"90","author":"LPF Garcia","year":"2015","unstructured":"Garcia LPF, S\u00e1ez JA, Luengo J, Lorena AC, de Carvalho ACPLF, Herrera F (2015b) Using the one-vs-one decomposition to improve the performance of class noise filters via an aggregation strategy in multi-class classification problems. Knowledge-Based Syst 90:153\u2013164","journal-title":"Knowledge-Based Syst"},{"key":"475_CR13","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.neucom.2014.12.100","volume":"176","author":"LPF Garcia","year":"2016","unstructured":"Garcia LPF, de Carvalho ACPLF, Lorena AC (2016) Noise detection in the meta-learning level. Neurocomputing 176:14\u201325","journal-title":"Neurocomputing"},{"key":"475_CR14","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, New York"},{"issue":"3","key":"475_CR15","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1023\/B:MACH.0000015878.60765.42","volume":"54","author":"CG Giraud-Carrier","year":"2004","unstructured":"Giraud-Carrier CG, Vilalta R, Brazdil P (2004) Introduction to the special issue on meta-learning. Machine Learning 54(3):187\u2013193","journal-title":"Machine Learning"},{"key":"475_CR16","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.4018\/978-1-60566-010-3.ch188","volume-title":"Encyclopedia of data warehousing and mining","author":"CG Giraud-Carrier","year":"2009","unstructured":"Giraud-Carrier CG, Brazdil P, Soares C, Vilalta R (2009) Meta-learning. In: Wang J (ed) Encyclopedia of data warehousing and mining. IGI Global, Hershey, pp 1207\u20131215"},{"key":"475_CR17","unstructured":"Hall MA (1999) Correlation-based feature selection for machine learning. Ph.D. thesis, University of Waikato"},{"key":"475_CR18","first-page":"83","volume-title":"The Morgan Kaufmann series in data management systems","author":"J Han","year":"2012","unstructured":"Han J, Kamber M, Pei J (2012) Data preprocessing. Data mining. The Morgan Kaufmann series in data management systems, 3rd edn. Morgan Kaufmann, Boston, pp 83\u2013124","edition":"3"},{"issue":"3","key":"475_CR19","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1109\/34.990132","volume":"24","author":"TK Ho","year":"2002","unstructured":"Ho TK, Basu M (2002) Complexity measures of supervised classification problems. IEEE Trans Pattern Anal Mach Intell 24(3):289\u2013300","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"475_CR20","doi-asserted-by":"crossref","first-page":"117","DOI":"10.3233\/HIS-2011-0133","volume":"8","author":"J Kanda","year":"2011","unstructured":"Kanda J, de Carvalho ACPLF, Hruschka ER, Soares C (2011) Selection of algorithms to solve traveling salesman problems using meta-learning. Int J Hybrid Intell Syst 8(3):117\u2013128","journal-title":"Int J Hybrid Intell Syst"},{"key":"475_CR21","doi-asserted-by":"crossref","unstructured":"Khoshgoftaar T, Rebours P (2004) Generating multiple noise elimination filters with the ensemble-partitioning filter. In: IEEE international conference on information reuse and integration, pp 369\u2013375","DOI":"10.1109\/IRI.2004.1431489"},{"key":"475_CR22","unstructured":"Lichman M (2013) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences. http:\/\/archive.ics.uci.edu\/ml"},{"key":"475_CR23","doi-asserted-by":"crossref","unstructured":"Lorena AC, Garcia LPF, 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":"475_CR24","doi-asserted-by":"crossref","unstructured":"Mantovani RG, Rossi ALD, Vanschoren J, Bischl B, de\u00a0Carvalho ACPLF (2015) To tune or not to tune: recommending when to adjust SVM hyper-parameters via meta-learning. In: International joint conference on neural networks (IJCNN), pp 1\u20138","DOI":"10.1109\/IJCNN.2015.7280644"},{"key":"475_CR25","volume-title":"Machine learning, neural and statistical classification","author":"D Michie","year":"1994","unstructured":"Michie D, Spiegelhalter DJ, Taylor CC (1994) Machine learning, neural and statistical classification. Ellis Horwood, Upper Saddle River"},{"key":"475_CR26","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2014.06.026","volume":"143","author":"PBC Miranda","year":"2014","unstructured":"Miranda PBC, Prud\u00eancio RBC, de Carvalho ACPLF, Soares C (2014) A hybrid meta-learning architecture for multi-objective optimization of SVM parameters. Neurocomputing 143:27\u201343","journal-title":"Neurocomputing"},{"key":"475_CR27","unstructured":"Orriols-Puig A, Maci\u00e1 N, Ho TK (2010) Documentation for the data complexity library in C++. Technical report. La Salle\u2014Universitat Ramon Llull"},{"key":"475_CR28","doi-asserted-by":"crossref","unstructured":"Peng Y, Flach PA, Soares C, Brazdil P (2002) Improved dataset characterisation for meta-learning. In: 5th international conference on discovery science, pp 141\u2013152","DOI":"10.1007\/3-540-36182-0_14"},{"key":"475_CR29","unstructured":"Pfahringer B, Bensusan H, Giraud-Carrier CG (2000) Meta-learning by landmarking various learning algorithms. In: 17th international conference on machine learning (ICML), pp 743\u2013750"},{"issue":"1","key":"475_CR30","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":"475_CR31","doi-asserted-by":"crossref","unstructured":"Rossi ALD, de\u00a0Carvalho ACPLF, Soares C (2012) Meta-learning for periodic algorithm selection in time-changing data. In: Brazilian symposium on neural networks (SBRN), pp 7\u201312","DOI":"10.1109\/SBRN.2012.50"},{"key":"475_CR32","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neucom.2013.05.048","volume":"127","author":"ALD Rossi","year":"2014","unstructured":"Rossi ALD, de Carvalho ACPLF, Soares C, de Souza BF (2014) Metastream: a meta-learning based method for periodic algorithm selection in time-changing data. Neurocomputing 127:52\u201364","journal-title":"Neurocomputing"},{"issue":"1","key":"475_CR33","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.patcog.2012.07.009","volume":"46","author":"JA S\u00e1ez","year":"2013","unstructured":"S\u00e1ez JA, Luengo J, Herrera F (2013) Predicting noise filtering efficacy with data complexity measures for nearest neighbor classification. Pattern Recognit 46(1):355\u2013364","journal-title":"Pattern Recognit"},{"key":"475_CR34","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"},{"key":"475_CR35","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. Inf Fusion 27:19\u201332","journal-title":"Inf Fusion"},{"key":"475_CR36","doi-asserted-by":"crossref","unstructured":"Schubert E, Wojdanowski R, Zimek A, Kriegel HP (2012) On evaluation of outlier rankings and outlier scores. In: 12th SIAM international conference on data mining (SDM), pp 1047\u20131058","DOI":"10.1137\/1.9781611972825.90"},{"key":"475_CR37","doi-asserted-by":"crossref","unstructured":"Sluban B, Gamberger D, Lavra\u010d N (2010) Advances in class noise detection. In: 19th European conference on artificial intelligence (ECAI), pp 1105\u20131106","DOI":"10.3233\/978-1-60750-606-5-1105"},{"issue":"2","key":"475_CR38","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"},{"issue":"1","key":"475_CR39","first-page":"6:1","volume":"41","author":"KA Smith-Miles","year":"2008","unstructured":"Smith-Miles KA (2008) Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput Surv 41(1):6:1\u20136:25","journal-title":"ACM Comput Surv"},{"key":"475_CR40","doi-asserted-by":"crossref","unstructured":"de\u00a0Souza BF, de\u00a0Carvalho ACPLF, Soares C (2010) Empirical evaluation of ranking prediction methods for gene expression data classification. In: 12th Ibero-American conference on artificial intelligence (IBERAMIA), pp 194\u2013203","DOI":"10.1007\/978-3-642-16952-6_20"},{"key":"475_CR41","unstructured":"Teng CM (1999) Correcting noisy data. In: 16th international conference on machine learning (ICML), pp 239\u2013248"},{"key":"475_CR42","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.knosys.2014.12.011","volume":"76","author":"RY Toledo","year":"2015","unstructured":"Toledo RY, Mota YC, Mart\u00ednez L (2015) Correcting noisy ratings in collaborative recommender systems. Knowledge-Based Syst 76:96\u2013108","journal-title":"Knowledge-Based Syst"},{"issue":"6","key":"475_CR43","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TSMC.1976.4309523","volume":"6","author":"I Tomek","year":"1976","unstructured":"Tomek I (1976) An experiment with the edited nearest-neighbor rule. IEEE Trans Syst Man Cybern 6(6):448\u2013452","journal-title":"IEEE Trans Syst Man Cybern"},{"key":"475_CR44","volume-title":"Statistical learning theory","author":"VN Vapnik","year":"1998","unstructured":"Vapnik VN (1998) Statistical learning theory. Wiley, New York"},{"key":"475_CR45","doi-asserted-by":"crossref","unstructured":"Verbaeten S, Assche AV (2003) Ensemble methods for noise elimination in classification problems. In: 4th international workshop multiple classifier systems, pp 317\u2013325","DOI":"10.1007\/3-540-44938-8_32"},{"issue":"3","key":"475_CR46","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1109\/TSMC.1972.4309137","volume":"2","author":"DL Wilson","year":"1972","unstructured":"Wilson DL (1972) Asymtoptic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cybern 2(3):408\u2013421","journal-title":"IEEE Trans Syst Man Cybern"},{"issue":"4","key":"475_CR47","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/TSMCA.2008.923034","volume":"38","author":"X Wu","year":"2008","unstructured":"Wu X, Zhu X (2008) Mining with noise knowledge: error-aware data mining. IEEE Trans Syst Man Cybern Part A 38(4):917\u2013932","journal-title":"IEEE Trans Syst Man Cybern Part A"},{"issue":"3","key":"475_CR48","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 (2004) Class noise vs. attribute noise: a quantitative study. Artif Intell Rev 22(3):177\u2013210","journal-title":"Artif Intell Rev"},{"key":"475_CR49","unstructured":"Zhu X, Wu X, Chen Q (2003) Eliminating class noise in large datasets. In: 20th international conference on machine learning (ICML), pp 920\u2013927"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-016-0475-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10618-016-0475-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-016-0475-9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:49:26Z","timestamp":1748998166000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10618-016-0475-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,7,13]]},"references-count":49,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2016,9]]}},"alternative-id":["475"],"URL":"https:\/\/doi.org\/10.1007\/s10618-016-0475-9","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"value":"1384-5810","type":"print"},{"value":"1573-756X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,7,13]]}}}