{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:28:58Z","timestamp":1780511338830,"version":"3.54.1"},"reference-count":86,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R006660\/1"],"award-info":[{"award-number":["EP\/R006660\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/R006660\/2"],"award-info":[{"award-number":["EP\/R006660\/2"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010669","name":"H2020 LEIT Information and Communication Technologies","doi-asserted-by":"publisher","award":["952215"],"award-info":[{"award-number":["952215"]}],"id":[{"id":"10.13039\/100010669","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":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Class imbalance introduces additional challenges when learning classifiers from concept drifting data streams. Most existing work focuses on designing new algorithms for dealing with the global imbalance ratio and does not consider other data complexities. Independent research on static imbalanced data has highlighted the influential role of local data difficulty factors such as minority class decomposition and presence of unsafe types of examples. Despite often being present in real-world data, the interactions between concept drifts and local data difficulty factors have not been investigated in concept drifting data streams yet. We thoroughly study the impact of such interactions on drifting imbalanced streams. For this purpose, we put forward a new categorization of concept drifts for class imbalanced problems. Through comprehensive experiments with synthetic and real data streams, we study the influence of concept drifts, global class imbalance, local data difficulty factors, and their combinations, on predictions of representative online classifiers. Experimental results reveal the high influence of new considered factors and their local drifts, as well as differences in existing classifiers\u2019 reactions to such factors. Combinations of multiple factors are the most challenging for classifiers. Although existing classifiers are partially capable of coping with global class imbalance, new approaches are needed to address challenges posed by imbalanced data streams.<\/jats:p>","DOI":"10.1007\/s10115-021-01560-w","type":"journal-article","created":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T18:02:41Z","timestamp":1617300161000},"page":"1429-1469","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["The impact of data difficulty factors on classification of imbalanced and concept drifting data streams"],"prefix":"10.1007","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9723-525X","authenticated-orcid":false,"given":"Dariusz","family":"Brzezinski","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2639-0671","authenticated-orcid":false,"given":"Leandro L.","family":"Minku","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomasz","family":"Pewinski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4949-8271","authenticated-orcid":false,"given":"Jerzy","family":"Stefanowski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0329-5022","authenticated-orcid":false,"given":"Artur","family":"Szumaczuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,1]]},"reference":[{"key":"1560_CR1","doi-asserted-by":"publisher","first-page":"553","DOI":"10.1016\/j.comcom.2020.01.061","volume":"153","author":"S Ancy","year":"2020","unstructured":"Ancy S, Paulraj D (2020) Handling imbalanced data with concept drift by applying dynamic sampling and ensemble classification model. Comput Commun 153:553\u2013560","journal-title":"Comput Commun"},{"key":"1560_CR2","first-page":"1601","volume":"11","author":"A Bifet","year":"2010","unstructured":"Bifet A, Holmes G, Kirkby R, Pfahringer B (2010) MOA: massive online analysis. J Mach Learn Res 11:1601\u20131604","journal-title":"J Mach Learn Res"},{"key":"1560_CR3","doi-asserted-by":"crossref","unstructured":"B\u0142aszczy\u0144ski J, Stefanowski J (2015) Neighbourhood sampling in bagging for imbalanced data. Neurocomputing 150 A:184\u2013203","DOI":"10.1016\/j.neucom.2014.07.064"},{"key":"1560_CR4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/978-3-319-67946-4_2","volume-title":"Advances in data analysis with computational intelligence methods","author":"J B\u0142aszczy\u0144ski","year":"2018","unstructured":"B\u0142aszczy\u0144ski J, Stefanowski J (2018) Local data characteristics in learning classifiers from imbalanced data. In: Kacprzyk J, Rutkowski L, Gaweda A, Yen G (eds) Advances in data analysis with computational intelligence methods. Studies in computational intelligence, Springer, New York, pp 51\u201385"},{"key":"1560_CR5","volume-title":"ACL","author":"Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th annual meeting of the association for computational linguistics, June 23\u201330","year":"2007","unstructured":"Blitzer J, Dredze M, Pereira F (2007) Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th annual meeting of the association for computational linguistics, June 23\u201330 (2007) ACL. Czech Republic, Prague"},{"issue":"2","key":"1560_CR6","first-page":"31","volume":"49","author":"P Branco","year":"2016","unstructured":"Branco P, Torgo L, Ribeiro R (2016) A survey of predictive modeling under imbalanced distributions. ACM Comput Surv 49(2):31","journal-title":"ACM Comput Surv"},{"issue":"2","key":"1560_CR7","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/BF00058655","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman L (1996) Bagging predictors. Mach Learn 24(2):123\u2013140","journal-title":"Mach Learn"},{"issue":"1","key":"1560_CR8","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1109\/TNNLS.2013.2251352","volume":"25","author":"D Brzezinski","year":"2014","unstructured":"Brzezinski D, Stefanowski J (2014) Reacting to different types of concept drift: the accuracy updated ensemble algorithm. IEEE Trans Neural Netw Learn Syst 25(1):81\u201394","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"2","key":"1560_CR9","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1007\/s10115-017-1022-8","volume":"52","author":"D Brzezinski","year":"2017","unstructured":"Brzezinski D, Stefanowski J (2017) Prequential auc: properties of the area under the roc curve for data streams with concept drift. Knowl Inf Syst 52(2):531\u2013562","journal-title":"Knowl Inf Syst"},{"key":"1560_CR10","doi-asserted-by":"publisher","unstructured":"Brzezinski D, Stefanowski J (2018) Ensemble classifiers for imbalanced and evolving data streams. World Scientific, Singapore, pp 44\u201368. https:\/\/doi.org\/10.1142\/9789813228047_0003","DOI":"10.1142\/9789813228047_0003"},{"key":"1560_CR11","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1016\/j.ins.2018.06.020","volume":"462","author":"D Brzezinski","year":"2018","unstructured":"Brzezinski D, Stefanowski J, Susmaga R, Szczech I (2018) Visual-based analysis of classification measures and their properties for class imbalanced problems. Inf Sci 462:242\u2013261","journal-title":"Inf Sci"},{"key":"1560_CR12","doi-asserted-by":"crossref","unstructured":"Brzezinski D, Stefanowski J, Susmaga R, Szczech I (2019) On the dynamics of classification measures for imbalanced and streaming data. IEEE Trans Neural Netw Learn Syst 31(8):2868\u20132878","DOI":"10.1109\/TNNLS.2019.2899061"},{"key":"1560_CR13","doi-asserted-by":"crossref","unstructured":"Cabral G, Minku L, Shihab E, Mujahid S (2019) Class imbalance evolution and verification latency in just-in-time software defect prediction. In: Proceedings of the international conference on software engineering (ICSE)","DOI":"10.1109\/ICSE.2019.00076"},{"key":"1560_CR14","doi-asserted-by":"crossref","unstructured":"Chen S, He H (2009) Sera: selectively recursive approach towards nonstationary imbalanced stream data mining. In: Proceedings of the 2009 international joint conference on neural networks, pp 522\u2013529","DOI":"10.1109\/IJCNN.2009.5178874"},{"key":"1560_CR15","doi-asserted-by":"crossref","unstructured":"Chen S, He H (2011) Towards incremental learning of nonstationary imbalanced data stream: a multiple selectively recursive approach. Evol Syst 2:35\u201350","DOI":"10.1007\/s12530-010-9021-y"},{"key":"1560_CR16","first-page":"1","volume":"7","author":"J Demsar","year":"2006","unstructured":"Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7:1\u201330","journal-title":"J Mach Learn Res"},{"issue":"10","key":"1560_CR17","doi-asserted-by":"publisher","first-page":"2283","DOI":"10.1109\/TKDE.2012.136","volume":"25","author":"G Ditzler","year":"2013","unstructured":"Ditzler G, Polikar R (2013) Incremental learning of concept drift from streaming imbalanced data. IEEE Trans Knowl Data Eng 25(10):2283\u20132301","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"4","key":"1560_CR18","doi-asserted-by":"publisher","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 Comp Int Mag 10(4):12\u201325","journal-title":"IEEE Comp Int Mag"},{"key":"1560_CR19","doi-asserted-by":"crossref","unstructured":"Domingos P, Hulten G (2000) Mining high-speed data streams. In: Proceedings of the 6th ACM sigkdd international conference on knowledge discovery and data mining, pp 71\u201380","DOI":"10.1145\/347090.347107"},{"key":"1560_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98074-4","volume-title":"Learning from imbalanced data sets","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Galar M, Prati RC, Krawczyk B, Herrera F (2018a) Learning from imbalanced data sets. Springer, New York"},{"key":"1560_CR21","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1613\/jair.1.11192","volume":"61","author":"A Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez A, Garc\u00eda S, Herrera F, Chawla NV (2018b) SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary. J Artif Intell Res 61:863\u2013905","journal-title":"J Artif Intell Res"},{"issue":"5814","key":"1560_CR22","doi-asserted-by":"publisher","first-page":"972","DOI":"10.1126\/science.1136800","volume":"315","author":"BJ Frey","year":"2007","unstructured":"Frey BJ, Dueck D (2007) Clustering by passing messages between data points. Science 315(5814):972\u2013976","journal-title":"Science"},{"key":"1560_CR23","doi-asserted-by":"publisher","DOI":"10.1201\/EBK1439826119","volume-title":"Knowledge discovery from data streams","author":"J Gama","year":"2010","unstructured":"Gama J (2010) Knowledge discovery from data streams. Chapman and Hall, London"},{"key":"1560_CR24","doi-asserted-by":"crossref","unstructured":"Gama J, Castillo G (2006) Learning with local drift detection. In: International conference on advanced data mining and applications, pp 42\u201355","DOI":"10.1007\/11811305_4"},{"issue":"3","key":"1560_CR25","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/s10994-012-5320-9","volume":"90","author":"J Gama","year":"2013","unstructured":"Gama J, Sebasti\u00e3o R, Rodrigues PP (2013) On evaluating stream learning algorithms. Mach Learn 90(3):317\u2013346","journal-title":"Mach Learn"},{"key":"1560_CR26","doi-asserted-by":"crossref","unstructured":"Gama J, Zliobaite I, Bifet A, Pechenizkiy M, Bouchachia A (2014) A survey on concept drift adaptation. ACM Comput Surv 46(4):44:1-44:37","DOI":"10.1145\/2523813"},{"key":"1560_CR27","doi-asserted-by":"crossref","unstructured":"Gao J, Fan W, Han J, Yu PS (2007) A general framework for mining concept-drifting data streams with skewed distributions. In: Proceedings of the 2007 SIAM international conference on data mining, pp 3\u201314","DOI":"10.1137\/1.9781611972771.1"},{"issue":"6","key":"1560_CR28","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1109\/MIC.2008.119","volume":"12","author":"J Gao","year":"2008","unstructured":"Gao J, Ding B, Han J, Fan W, Yu PS (2008) Classifying data streams with skewed class distributions and concept drifts. IEEE Internet Comput 12(6):37\u201349","journal-title":"IEEE Internet Comput"},{"key":"1560_CR29","doi-asserted-by":"crossref","unstructured":"Garcia V, Sanchez J, Mollineda R (2007) An empirical study of the behaviour of classifiers on imbalanced and overlapped data sets. In: Proceeding of progress in pattern recognition, image analysis and applications, LNCS, vol 4756. Springer, pp 397\u2013406","DOI":"10.1007\/978-3-540-76725-1_42"},{"issue":"2","key":"1560_CR30","doi-asserted-by":"publisher","first-page":"119","DOI":"10.1007\/s12530-013-9076-7","volume":"4","author":"A Ghazikhani","year":"2013","unstructured":"Ghazikhani A, Monsefi R, Yazdi H (2013) Recursive least square perceptron model for non-stationary and imbalanced data stream classification. Evol Syst 4(2):119\u2013131","journal-title":"Evol Syst"},{"issue":"1","key":"1560_CR31","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1007\/s13042-013-0180-6","volume":"5","author":"A Ghazikhani","year":"2014","unstructured":"Ghazikhani A, Monsefi R, Yazdi H (2014) Online neural network model for non-stationary and imbalanced data stream classification. Int J Mach Learn Cybern 5(1):51\u201362","journal-title":"Int J Mach Learn Cybern"},{"key":"1560_CR32","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1007\/s10115-018-1257-z","volume":"60","author":"I Goldenberg","year":"2019","unstructured":"Goldenberg I, Webb G (2019) Survey of distance measures for quantifying concept drift and shift in numeric data. Knowl Inf Syst 60:591\u2013615","journal-title":"Knowl Inf Syst"},{"key":"1560_CR33","doi-asserted-by":"crossref","unstructured":"Gomes H, Barddal J, Enembreck F, Bifet A (2017) A survey on ensemble learning for data stream classification. ACM Comput Surv 50(2):23:1\u201336","DOI":"10.1145\/3054925"},{"key":"1560_CR34","volume-title":"Imbalanced learning: foundations, algorithms, and applications","year":"2013","unstructured":"He H, Ma Y (eds) (2013) Imbalanced learning: foundations, algorithms, and applications. Wiley, New York"},{"key":"1560_CR35","doi-asserted-by":"crossref","unstructured":"Hoens T, Chawla V (2013) Learning in non-stationary environments with class imbalance. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp 168\u2013176","DOI":"10.1145\/2339530.2339558"},{"issue":"5","key":"1560_CR36","doi-asserted-by":"publisher","first-page":"429","DOI":"10.3233\/IDA-2002-6504","volume":"6","author":"N Japkowicz","year":"2002","unstructured":"Japkowicz N, Stephen S (2002) The class imbalance problem: a systematic study. Intell Data Anal 6(5):429\u2013449","journal-title":"Intell Data Anal"},{"issue":"1","key":"1560_CR37","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1145\/1007730.1007737","volume":"6","author":"T Jo","year":"2004","unstructured":"Jo T, Japkowicz N (2004) Class imbalances versus small disjuncts. SIGKDD Explor 6(1):40\u201349","journal-title":"SIGKDD Explor"},{"issue":"1","key":"1560_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12530-016-9168-2","volume":"9","author":"I Khamassi","year":"2018","unstructured":"Khamassi I, Sayed-Mouchaweh M, Hammami M, Gh\u00e9dira K (2018) Discussion and review on evolving data streams and concept drift adapting. Evol Syst 9(1):1\u201323","journal-title":"Evol Syst"},{"key":"1560_CR39","doi-asserted-by":"crossref","unstructured":"Krawczyk B, Skryjomski P (2017) Cost-sensitive perceptron decision trees for imbalanced drifting data streams. In: Machine learning and knowledge discovery in databases: European conference, ECML PKDD 2017, Skopje, Macedonia, September 18\u201322, 2017, Proceedings, Part II, pp 512\u2013527","DOI":"10.1007\/978-3-319-71246-8_31"},{"key":"1560_CR40","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.inffus.2017.02.004","volume":"37","author":"B Krawczyk","year":"2017","unstructured":"Krawczyk B, Minku L, Gama J, Stefanowski J, Wo\u017aniak M (2017) Ensemble learning for data stream analysis: a survey. Inf Fusion 37:132\u2013156","journal-title":"Inf Fusion"},{"key":"1560_CR41","unstructured":"Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-side selection. In: Proceedings of the 14th international conference on machine learning ICML-97, pp 179\u2013186"},{"issue":"2","key":"1560_CR42","doi-asserted-by":"publisher","first-page":"151","DOI":"10.2478\/fcds-2019-0009","volume":"44","author":"M Lango","year":"2019","unstructured":"Lango M (2019) Tackling the problem of class imbalance in multi-class sentiment classification: an experimental study. Found Comput Decis Sci 44(2):151\u2013178","journal-title":"Found Comput Decis Sci"},{"key":"1560_CR43","doi-asserted-by":"publisher","unstructured":"Laurikkala J (2001) Improving identification of difficult small classes by balancing class distribution. Technical report A-2001-2, University of Tampere, https:\/\/doi.org\/10.1007\/3-540-48229-6_9","DOI":"10.1007\/3-540-48229-6_9"},{"key":"1560_CR44","doi-asserted-by":"crossref","unstructured":"Levin D, Peres Y, Wilmer E (2008) Markov chains and mixing times, 2nd edn. American Mathematical Society, Rhode Island","DOI":"10.1090\/mbk\/058"},{"key":"1560_CR45","doi-asserted-by":"crossref","unstructured":"Lichtenwalter R, Chawla N (2010) Adaptive methods for classification in arbitrarily imbalanced and drifting data streams. In: New frontiers in applied data mining. Lecture notes in computer science, vol 5669, pp 53\u201375. https:\/\/doi.org\/10.1007\/978-3-642-14640-4_5","DOI":"10.1007\/978-3-642-14640-4_5"},{"key":"1560_CR46","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.ins.2013.09.038","volume":"257","author":"V Lopez","year":"2014","unstructured":"Lopez V, Fernandez A, Garcia S, Palade V, Herrera F (2014) An insight into classification with imbalanced data: empirical results and current trends on using data intrinsic characteristics. Inf Sci 257:113\u2013141","journal-title":"Inf Sci"},{"key":"1560_CR47","doi-asserted-by":"crossref","unstructured":"Lu Y, Cheung YM, Tang Y (2017) Dynamic weighted majority for incremental learning of imbalanced data streams with concept drift. In: International joint conference on artificial intelligence, pp 53\u201375","DOI":"10.24963\/ijcai.2017\/333"},{"key":"1560_CR48","doi-asserted-by":"crossref","unstructured":"Lyon RJ, Brooke JM, Knowles JD, Stappers BW (2014) Hellinger distance trees for imbalanced streams. CoRR arXiv:1405.2278","DOI":"10.1109\/ICPR.2014.344"},{"issue":"4","key":"1560_CR49","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1109\/TKDE.2011.58","volume":"24","author":"L Minku","year":"2012","unstructured":"Minku L, Yao X (2012) DDD: a new ensemble approach for dealing with concept drift. IEEE Trans Knowl Data Eng 24(4):619\u2013633","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1560_CR50","doi-asserted-by":"publisher","first-page":"730","DOI":"10.1109\/TKDE.2009.156","volume":"22","author":"L Minku","year":"2010","unstructured":"Minku L, White A, Yao X (2010) The impact of diversity on on-line ensemble learning in the presence of concept drift. IEEE Trans Knowl Data Eng 22:730\u2013742","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1560_CR51","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-319-89803-2_2","volume-title":"Learning from data streams in evolving environments: methods and applications","author":"LL Minku","year":"2019","unstructured":"Minku LL (2019) Transfer learning in non-stationary environments. In: Sayed-Mouchaweh M (ed) Learning from data streams in evolving environments: methods and applications. Springer, Cham, pp 13\u201337"},{"key":"1560_CR52","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.neucom.2014.03.075","volume":"149","author":"B Mirza","year":"2015","unstructured":"Mirza B, Lin Z, Liu N (2015) Ensemble of subset online sequential extreme learning machine for class imbalance and concept drift. Neurocomputing 149:316\u2013329","journal-title":"Neurocomputing"},{"key":"1560_CR53","doi-asserted-by":"crossref","unstructured":"Nakov P, Ritter A, Rosenthal S, Sebastiani F, Stoyanov V (2016) Semeval-2016 task 4: sentiment analysis in twitter. In: Proceedings of the 10th international workshop on semantic evaluation, SemEval@NAACL-HLT 2016, San Diego, CA, USA, June 16\u201317, 2016, pp 1\u201318","DOI":"10.18653\/v1\/S16-1001"},{"key":"1560_CR54","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1007\/s10844-011-0193-0","volume":"39","author":"K Napiera\u0142a","year":"2012","unstructured":"Napiera\u0142a K, Stefanowski J (2012a) BRACID: a comprehensive approach to learning rules from imbalanced data. J Intell Inf Syst 39:335\u2013373","journal-title":"J Intell Inf Syst"},{"key":"1560_CR55","doi-asserted-by":"crossref","unstructured":"Napiera\u0142a K, Stefanowski J (2012b) The influence of minority class distribution on learning from imbalance data. In: Proceedings of the 7th international conference on HAIS 2012, pp 139\u2013150","DOI":"10.1007\/978-3-642-28931-6_14"},{"issue":"3","key":"1560_CR56","doi-asserted-by":"publisher","first-page":"563","DOI":"10.1007\/s10844-015-0368-1","volume":"46","author":"K Napierala","year":"2016","unstructured":"Napierala K, Stefanowski J (2016) Types of minority class examples and their influence on learning classifiers from imbalanced data. J Intell Inf Syst 46(3):563\u2013597","journal-title":"J Intell Inf Syst"},{"key":"1560_CR57","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: Proceedings of 7th international conference RSCTC 2010, LNAI, Springer, vol 6086, pp 158\u2013167","DOI":"10.1007\/978-3-642-13529-3_18"},{"key":"1560_CR58","unstructured":"Nickerson A, Japkowicz N, Milios E (2001) Using unsupervised learning to guide re-sampling in imbalanced data sets. In: Proceedings of the eighth international workshop on AI and statitsics"},{"key":"1560_CR59","doi-asserted-by":"crossref","unstructured":"Olaitan OM, Viktor HL (2018) SCUT-DS: learning from multi-class imbalanced canadian weather data. In: Foundations of intelligent systems\u201424th international symposium, ISMIS 2018, Limassol, Cyprus, October 29\u201331, 2018, Proceedings, pp 291\u2013301","DOI":"10.1007\/978-3-030-01851-1_28"},{"key":"1560_CR60","first-page":"105","volume-title":"Eighth international workshop on artificial intelligence and statistics","author":"NC Oza","year":"2001","unstructured":"Oza NC, Russell S (2001a) Online bagging and boosting. In: Jaakkola T, Richardson T (eds) Eighth international workshop on artificial intelligence and statistics. Morgan Kaufmann, Key West, Florida, pp 105\u2013112"},{"key":"1560_CR61","doi-asserted-by":"crossref","unstructured":"Oza NC, Russell SJ (2001b) Experimental comparisons of online and batch versions of bagging and boosting. In: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, San Francisco, CA, USA, August 26\u201329, 2001, pp 359\u2013364","DOI":"10.1145\/502512.502565"},{"key":"1560_CR62","doi-asserted-by":"crossref","unstructured":"Prati R, Batista G, Monard M (2004) Class imbalance versus class overlapping: an analysis of a learning system behavior. In: Proceedings of the 3rd Mexican international conference on artificial intelligence, pp 312\u2013321","DOI":"10.1007\/978-3-540-24694-7_32"},{"key":"1560_CR63","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.neucom.2018.01.063","volume":"286","author":"S Ren","year":"2018","unstructured":"Ren S, Liao B, Zhu W, Li Z, Liu W, Li K (2018) The gradual resampling ensemble for mining imbalanced data streams with concept drift. Neurocomputing 286:150\u2013166","journal-title":"Neurocomputing"},{"key":"1560_CR64","doi-asserted-by":"crossref","unstructured":"Sarnelle J, Sanchez A, Capo R, Haas J, Polikar R (2015) Quantifying the limited and gradual concept drift assumption. In: Proceedings of the 2015 international joint conference on neural networks (IJCNN)","DOI":"10.1109\/IJCNN.2015.7280850"},{"key":"1560_CR65","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1016\/j.eswa.2015.09.055","volume":"45","author":"MR Sousa","year":"2016","unstructured":"Sousa MR, Gama J, Brandao E (2016) A new dynamic modeling framework for credit risk assessment. Expert Syst Appl 45:341\u2013351","journal-title":"Expert Syst Appl"},{"key":"1560_CR66","doi-asserted-by":"crossref","unstructured":"Spiliopoulou M, Ntoutsi E, Theodoridis Y, Schult R (2006) MONIC: modeling and monitoring cluster transitions. In: Proceedings of the 12th ACM SIGKDD international conference on knowledge discovery and data mining, pp 706\u2013711","DOI":"10.1145\/1150402.1150491"},{"key":"1560_CR67","doi-asserted-by":"crossref","unstructured":"Spiliopoulou M, Ntoutsi E, Theodoridis Y, Schult R (2013) MONIC and followups on modeling and monitoring cluster transigons. In: European conference on machine learning and principles and practice of knowledge discovery in databases (ECML\/PKDD), pp 622\u2013626","DOI":"10.1007\/978-3-642-40994-3_41"},{"key":"1560_CR68","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/978-3-642-28699-5_11","volume-title":"Emerging paradigms in machine learning","author":"J Stefanowski","year":"2013","unstructured":"Stefanowski J (2013) Overlapping, rare examples and class decomposition in learning classifiers from imbalanced data. In: Ramanna S, Jain LC, Howlett RJ (eds) Emerging paradigms in machine learning, vol 13. Springer, New York, pp 277\u2013306"},{"key":"1560_CR69","doi-asserted-by":"publisher","first-page":"333","DOI":"10.1007\/978-3-319-18781-5_17","volume-title":"Challenges in computational statistics and data mining","author":"J Stefanowski","year":"2016","unstructured":"Stefanowski J (2016) Dealing with data difficulty factors while learning from imbalanced data. In: Matwin S, Mielniczuk J (eds) Challenges in computational statistics and data mining. Springer, New York, pp 333\u2013363"},{"key":"1560_CR70","doi-asserted-by":"crossref","unstructured":"Street WN, Kim Y (2001) A streaming ensemble algorithm (SEA) for large-scale classification. In: Proceedings of the 7th ACM SIGKDD international conference on knowledge discovery data mining, pp 377\u2013382","DOI":"10.1145\/502512.502568"},{"issue":"6","key":"1560_CR71","doi-asserted-by":"publisher","first-page":"1532","DOI":"10.1109\/TKDE.2016.2526675","volume":"28","author":"Y Sun","year":"2016","unstructured":"Sun Y, Tang K, Minku L, 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":"1560_CR72","unstructured":"Theeramunkong T, Kijsirikul B, Cercone N, Ho TB (2009) PAKDD data mining competition. http:\/\/sede.neurotech.com.br\/PAKDD2009"},{"key":"1560_CR73","doi-asserted-by":"publisher","DOI":"10.7551\/mitpress\/1763.001.0001","volume-title":"Cellular automata machines: a new environment for modeling","author":"T Toffoli","year":"1987","unstructured":"Toffoli T, Margolus N (1987) Cellular automata machines: a new environment for modeling. MIT Press, Cambridge"},{"key":"1560_CR74","doi-asserted-by":"crossref","unstructured":"Wang H, Lu Y, Zhai C (2010) Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining, Washington, DC, USA, July 25\u201328, 2010, pp 783\u2013792","DOI":"10.1145\/1835804.1835903"},{"key":"1560_CR75","doi-asserted-by":"crossref","unstructured":"Wang S, Minku L, Yao X (2013) Concept drift detection for online class imbalance learning. In: Proceedings of the 2013 international joint conference on neural networks (IJCNN\u201913), pp 1\u20138","DOI":"10.1109\/IJCNN.2013.6706768"},{"issue":"5","key":"1560_CR76","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.1109\/TKDE.2014.2345380","volume":"27","author":"S Wang","year":"2015","unstructured":"Wang S, Minku LL, Yao X (2015) Resampling-based ensemble methods for online class imbalance learning. IEEE Trans Knowl Data Eng 27(5):1356\u20131368","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"1560_CR77","unstructured":"Wang S, Minku L, Yao X (2016) Dealing with multiple classes in online class imbalance learning. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence (IJCAI-16), pp 2118\u20132124"},{"issue":"10","key":"1560_CR78","doi-asserted-by":"publisher","first-page":"4802","DOI":"10.1109\/TNNLS.2017.2771290","volume":"29","author":"S Wang","year":"2018","unstructured":"Wang S, Minku LL, Yao X (2018) A systematic study of online class imbalance learning with concept drift. IEEE Trans Neural Netw Learn Syst 29(10):4802\u20134821","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"4","key":"1560_CR79","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1007\/s10618-015-0448-4","volume":"30","author":"GI Webb","year":"2016","unstructured":"Webb GI, Hyde R, Cao H, Nguyen H, Petitjean F (2016) Characterizing concept drift. Data Min Knowl Discov 30(4):964\u2013994","journal-title":"Data Min Knowl Discov"},{"issue":"5","key":"1560_CR80","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1007\/s10618-018-0554-1","volume":"32","author":"GI Webb","year":"2018","unstructured":"Webb GI, Lee LK, Goethals B, Petitjean F (2018) Analyzing concept drid and shid from sample data. Data Min Knowl Discov 32(5):1179\u20131199","journal-title":"Data Min Knowl Discov"},{"key":"1560_CR81","doi-asserted-by":"publisher","unstructured":"Weiss GM (2010) The impact of small disjuncts on classifier learning. In: Stahlbock R, Crone S, Lessmann S (eds) Data Mining. Annals of Information Systems, vol 8. Springer, Boston, MA, pp 193\u2013226. https:\/\/doi.org\/10.1007\/978-1-4419-1280-0_9","DOI":"10.1007\/978-1-4419-1280-0_9"},{"key":"1560_CR82","doi-asserted-by":"crossref","unstructured":"Wu K, Edwards A, Fan W, Gao J, Zhang K (2014) Classifying imbalanced data streams via dynamic feature group weighting with importance sampling. In: Proceedings of the 2014 SIAM international conference on data mining, pp 722\u2013730","DOI":"10.1137\/1.9781611973440.83"},{"key":"1560_CR83","doi-asserted-by":"publisher","unstructured":"Zhang H, Liu W, Wang S, Shan J, Liu Q (2019) Resample-based ensemble framework for drifting imbalanced data streams. In: Data Mining IEEE Access 7:65103\u201365115. https:\/\/doi.org\/10.1007\/978-1-4419-1280-0_9","DOI":"10.1007\/978-1-4419-1280-0_9"},{"key":"1560_CR84","doi-asserted-by":"crossref","unstructured":"Zliobaite I (2014) Controlled permutations for testing adaptive learning models. Knowl Inf Syst 39:565\u2013578","DOI":"10.1007\/s10115-013-0629-7"},{"key":"1560_CR85","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.neucom.2014.05.084","volume":"150","author":"I Zliobaite","year":"2015","unstructured":"Zliobaite I, Budka M, Stahl F (2015a) Towards cost-sensitive adaptation: when is it worth updating your predictive model? Neurocomputing 150:240\u2013249","journal-title":"Neurocomputing"},{"key":"1560_CR86","volume-title":"Big data analysis: new algorithms for a new society","author":"I Zliobaite","year":"2015","unstructured":"Zliobaite I, Pechenizkiy M, Gama J (2015b) An overview of concept drift applications. In: Japkowicz N, Stefanowski J (eds) Big data analysis: new algorithms for a new society. Springer, New York"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01560-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10115-021-01560-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-021-01560-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,31]],"date-time":"2023-01-31T07:26:00Z","timestamp":1675149960000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10115-021-01560-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,1]]},"references-count":86,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2021,6]]}},"alternative-id":["1560"],"URL":"https:\/\/doi.org\/10.1007\/s10115-021-01560-w","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,1]]},"assertion":[{"value":"8 April 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 March 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 March 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 April 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}