An iterative boosting-based ensemble for streaming data classification
•The IBS ensemble bases on iteratively applying boosting to learn from data stream.•IBS adjusts to new concept by gathering knowledge according to its current accuracy.•Adding more base learners when accuracy is low helps IBS recover fast from drifts.•IBS join features from batch and online algorith...
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| Vydané v: | Information fusion Ročník 45; s. 66 - 78 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.01.2019
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| ISSN: | 1566-2535, 1872-6305 |
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| Abstract | •The IBS ensemble bases on iteratively applying boosting to learn from data stream.•IBS adjusts to new concept by gathering knowledge according to its current accuracy.•Adding more base learners when accuracy is low helps IBS recover fast from drifts.•IBS join features from batch and online algorithms, as fast learning and flexibility.•Results show IBS is effective and low cost to handle classification in data stream.
Among the many issues related to data stream applications, those involved in predictive tasks such as classification and regression, play a significant role in Machine Learning (ML). The so-called ensemble-based approaches have characteristics that can be appealing to data stream applications, such as easy updating and high flexibility. In spite of that, some of the current approaches consider unsuitable ways of updating the ensemble along with the continuous stream processing, such as growing it indefinitely or deleting all its base learners when trying to overcome a concept drift. Such inadequate actions interfere with two inherent characteristics of data streams namely, its possible infinite length and its need for prompt responses. In this paper, a new ensemble-based algorithm, suitable for classification tasks, is proposed. It relies on applying boosting to new batches of data aiming at maintaining the ensemble by adding a certain number of base learners, which is established as a function of the current ensemble accuracy rate. The updating mechanism enhances the model flexibility, allowing the ensemble to gather knowledge fast to quickly overcome high error rates, due to concept drift, while maintaining satisfactory results by slowing down the updating rate in stable concepts. Results comparing the proposed ensemble-based algorithm against eight other ensembles found in the literature show that the proposed algorithm is very competitive when dealing with data stream classification. |
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| AbstractList | •The IBS ensemble bases on iteratively applying boosting to learn from data stream.•IBS adjusts to new concept by gathering knowledge according to its current accuracy.•Adding more base learners when accuracy is low helps IBS recover fast from drifts.•IBS join features from batch and online algorithms, as fast learning and flexibility.•Results show IBS is effective and low cost to handle classification in data stream.
Among the many issues related to data stream applications, those involved in predictive tasks such as classification and regression, play a significant role in Machine Learning (ML). The so-called ensemble-based approaches have characteristics that can be appealing to data stream applications, such as easy updating and high flexibility. In spite of that, some of the current approaches consider unsuitable ways of updating the ensemble along with the continuous stream processing, such as growing it indefinitely or deleting all its base learners when trying to overcome a concept drift. Such inadequate actions interfere with two inherent characteristics of data streams namely, its possible infinite length and its need for prompt responses. In this paper, a new ensemble-based algorithm, suitable for classification tasks, is proposed. It relies on applying boosting to new batches of data aiming at maintaining the ensemble by adding a certain number of base learners, which is established as a function of the current ensemble accuracy rate. The updating mechanism enhances the model flexibility, allowing the ensemble to gather knowledge fast to quickly overcome high error rates, due to concept drift, while maintaining satisfactory results by slowing down the updating rate in stable concepts. Results comparing the proposed ensemble-based algorithm against eight other ensembles found in the literature show that the proposed algorithm is very competitive when dealing with data stream classification. |
| Author | Bertini Junior, João Roberto Nicoletti, Maria do Carmo |
| Author_xml | – sequence: 1 givenname: João Roberto surname: Bertini Junior fullname: Bertini Junior, João Roberto email: bertini@ft.unicamp.br organization: School of Technology, University of Campinas, SP, Rua Paschoal Marmo, 1888, Jd. Nova Itália, Limeira, Brazil – sequence: 2 givenname: Maria do Carmo surname: Nicoletti fullname: Nicoletti, Maria do Carmo email: carmo@cc.faccamp.br organization: FACCAMP, Campo Limpo Paulista, SP, Brazil |
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| Cites_doi | 10.1016/j.ins.2016.10.028 10.1016/j.inffus.2013.04.006 10.1145/2523813 10.1006/jcss.1997.1504 10.1109/TNN.2011.2160459 10.1016/j.inffus.2017.09.005 10.1007/BF00116900 10.1006/inco.1994.1009 10.1016/j.inffus.2017.02.004 10.1007/s10994-012-5320-9 10.1109/TNNLS.2013.2251352 10.1145/1083784.1083789 10.1109/TNNLS.2013.2239309 10.1016/j.artint.2003.04.001 10.1016/j.inffus.2017.02.007 10.1109/MCI.2015.2471196 10.1023/A:1007661119649 10.1016/S1566-2535(02)00093-3 10.1016/j.inffus.2017.11.011 10.1007/BF00058655 10.3233/IDA-2007-11102 |
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| References | M. Lichman, UCI Machine Learning Repository, 2013. URL Scholz, Klinkenberg (bib0027) 2007; 11 Oza, Russell (bib0018) 2001 Widmer, Kubat (bib0009) 1996; 23 Xu, Sun (bib0023) 2010 Kolter, Maloof (bib0033) 2007; 8 Maloof, Michalski (bib0010) 2004; 154 Zhao, Xie, Xu, Sun (bib0024) 2017; 38 Gama, Žliobaite, Bifet, Pechenizkiy, Bouchachia (bib0005) 2014; 46 Aggarwal (bib0003) 2010 Pelossof, Jones, Vovsha, Rudin (bib0020) 2009 Brzeziński, Stefanowski (bib0042) 2011; 6679 Gama (bib0006) 2010 Pocock, Yiapanis, Singer, Lujan, Brown (bib0021) 2010 Bouguelia, Karlsson, Pashami, Nowaczyk, Holst (bib0029) 2018; 43 Quinlan (bib0037) 1993 . Li, Zhang, Shum, Zhang (bib0022) 2003 Maloof, Michalski (bib0039) 2000; 41 Breiman (bib0014) 1996; 24 S. Hettich, S. Bay, The UCI KDD archive, 1999, (University of California, Irvine, School of Information and Computer Sciences). Chu, Zaniolo (bib0025) 2004 Littlestone, Warmuth (bib0044) 1994; 108 Krawczyk, Minku, Gama, Stefanowski, Woźniak (bib0012) 2017; 37 Street, Kim (bib0034) 2001 Schlimmer, Granger (bib0004) 1986 Kidera, Ozawa, Abe (bib0026) 2006 Alippi, Borrachi, Roveri (bib0040) 2013; 24 Brzeziński, Stefanowski (bib0041) 2014; 25 Aggarwal (Ed.) (bib0007) 2007 Ditzler, Roveri, Alippi, Polikar (bib0001) 2015; 10 Carcillo, Dal Pozzolo, Le Borgne, Caelen, Mazzer, Bontempi (bib0031) 2018; 41 Pietruczuk, Rutkowski, Jaworski, Duda (bib0043) 2017; 381 Freund, Schapire (bib0015) 1996 Jaber, Cornuéjols, Tarroux (bib0045) 2013; 8227 Breiman, Friedman, Olshen, Stone (bib0050) 1984 Freund, Schapire (bib0046) 1997; 55 Gama, Medas, Castillo, Rodrigues (bib0049) 2004; 3171 Gaber, Zaslavsky, Krishnaswamy (bib0002) 2005; 34 Read, Bifet, Pfahringer, Holmes (bib0011) 2012; 7619 Kuncheva, Skurichina, Duin (bib0017) 2002; 3 Gama, Sebastiao, Rodrigues (bib0048) 2013; 90 Elwell, Polikar (bib0028) 2011; 22 Nguyen, Woon, Ng (bib0008) 2014 Ramamurthy, Bhatnagar (bib0036) 2007 Oza, Russell (bib0019) 2001 Xingquan, Xindong, Ying (bib0032) 2004 Wang, Fan, Yu, Han (bib0035) 2003 Hulten, Spencer, Domingos (bib0038) 2001 Woźniak, Cyganek (bib0030) 2016; 9714 Bifet, Holmes, Pfahringer, Gavaldà (bib0047) 2009 Harries (bib0051) 1999 Woźniak, Grana, Corchado (bib0013) 2014; 16 Freund, Schapire (bib0016) 1997; 55 Chu (10.1016/j.inffus.2018.01.003_bib0025) 2004 Oza (10.1016/j.inffus.2018.01.003_bib0019) 2001 Alippi (10.1016/j.inffus.2018.01.003_bib0040) 2013; 24 10.1016/j.inffus.2018.01.003_bib0052 10.1016/j.inffus.2018.01.003_bib0053 Li (10.1016/j.inffus.2018.01.003_bib0022) 2003 Ramamurthy (10.1016/j.inffus.2018.01.003_bib0036) 2007 Scholz (10.1016/j.inffus.2018.01.003_bib0027) 2007; 11 Gaber (10.1016/j.inffus.2018.01.003_bib0002) 2005; 34 Bouguelia (10.1016/j.inffus.2018.01.003_bib0029) 2018; 43 Street (10.1016/j.inffus.2018.01.003_bib0034) 2001 Read (10.1016/j.inffus.2018.01.003_bib0011) 2012; 7619 Wang (10.1016/j.inffus.2018.01.003_bib0035) 2003 Gama (10.1016/j.inffus.2018.01.003_bib0005) 2014; 46 Aggarwal (10.1016/j.inffus.2018.01.003_bib0003) 2010 Woźniak (10.1016/j.inffus.2018.01.003_bib0013) 2014; 16 Kidera (10.1016/j.inffus.2018.01.003_bib0026) 2006 Freund (10.1016/j.inffus.2018.01.003_bib0015) 1996 Gama (10.1016/j.inffus.2018.01.003_bib0006) 2010 Zhao (10.1016/j.inffus.2018.01.003_bib0024) 2017; 38 Kolter (10.1016/j.inffus.2018.01.003_bib0033) 2007; 8 Kuncheva (10.1016/j.inffus.2018.01.003_bib0017) 2002; 3 Freund (10.1016/j.inffus.2018.01.003_bib0016) 1997; 55 Quinlan (10.1016/j.inffus.2018.01.003_bib0037) 1993 Harries (10.1016/j.inffus.2018.01.003_bib0051) 1999 Jaber (10.1016/j.inffus.2018.01.003_bib0045) 2013; 8227 Brzeziński (10.1016/j.inffus.2018.01.003_bib0041) 2014; 25 Littlestone (10.1016/j.inffus.2018.01.003_bib0044) 1994; 108 Freund (10.1016/j.inffus.2018.01.003_bib0046) 1997; 55 Elwell (10.1016/j.inffus.2018.01.003_bib0028) 2011; 22 Maloof (10.1016/j.inffus.2018.01.003_bib0039) 2000; 41 Widmer (10.1016/j.inffus.2018.01.003_bib0009) 1996; 23 Hulten (10.1016/j.inffus.2018.01.003_bib0038) 2001 Xingquan (10.1016/j.inffus.2018.01.003_bib0032) 2004 Ditzler (10.1016/j.inffus.2018.01.003_bib0001) 2015; 10 Carcillo (10.1016/j.inffus.2018.01.003_bib0031) 2018; 41 Brzeziński (10.1016/j.inffus.2018.01.003_bib0042) 2011; 6679 Aggarwal (Ed.) (10.1016/j.inffus.2018.01.003_bib0007) 2007 Oza (10.1016/j.inffus.2018.01.003_bib0018) 2001 Breiman (10.1016/j.inffus.2018.01.003_bib0014) 1996; 24 Nguyen (10.1016/j.inffus.2018.01.003_bib0008) 2014 Pocock (10.1016/j.inffus.2018.01.003_bib0021) 2010 Breiman (10.1016/j.inffus.2018.01.003_bib0050) 1984 Xu (10.1016/j.inffus.2018.01.003_bib0023) 2010 Schlimmer (10.1016/j.inffus.2018.01.003_bib0004) 1986 Pietruczuk (10.1016/j.inffus.2018.01.003_bib0043) 2017; 381 Bifet (10.1016/j.inffus.2018.01.003_bib0047) 2009 Krawczyk (10.1016/j.inffus.2018.01.003_bib0012) 2017; 37 Gama (10.1016/j.inffus.2018.01.003_bib0048) 2013; 90 Pelossof (10.1016/j.inffus.2018.01.003_bib0020) 2009 Maloof (10.1016/j.inffus.2018.01.003_bib0010) 2004; 154 Gama (10.1016/j.inffus.2018.01.003_bib0049) 2004; 3171 Woźniak (10.1016/j.inffus.2018.01.003_bib0030) 2016; 9714 |
| References_xml | – start-page: 97 year: 2001 end-page: 106 ident: bib0038 article-title: Mining time-changing data streams publication-title: Proceedings of the International Conference on Knowledge Discovery and Data Mining – volume: 43 start-page: 33 year: 2018 end-page: 46 ident: bib0029 article-title: Mode tracking using multiple data streams publication-title: Inf. Fusion – year: 1999 ident: bib0051 article-title: Splice-2 comparative evaluation: electricity pricing publication-title: Technical Report – volume: 38 start-page: 43 year: 2017 end-page: 54 ident: bib0024 article-title: Multi-view learning overview: recent progress and new challenges publication-title: Inf. Fusion – volume: 108 start-page: 212 year: 1994 end-page: 261 ident: bib0044 article-title: The weighted majority algorithm publication-title: Inf. Comput. – volume: 7619 start-page: 313 year: 2012 end-page: 323 ident: bib0011 article-title: Batch-incremental versus instance-incremental learning in dynamic and evolving data publication-title: Advances in Intelligent Data Analysis XI – volume: 11 start-page: 3 year: 2007 end-page: 28 ident: bib0027 article-title: Boosting classifiers for drifting concepts publication-title: Intell. Data Anal. – start-page: 105 year: 2001 end-page: 112 ident: bib0018 article-title: Online bagging and boosting publication-title: Artificial Intelligence and Statistics – start-page: 226 year: 2003 end-page: 235 ident: bib0035 article-title: Mining concept-drifting data streams using ensemble classifiers publication-title: Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD) – volume: 6679 start-page: 155 year: 2011 end-page: 163 ident: bib0042 article-title: Accuracy updated ensemble for data streams with concept drift publication-title: Proceedings of the International Conference on Hybrid Artificial Intelligent Systems - Volume Part II – volume: 41 start-page: 27 year: 2000 end-page: 52 ident: bib0039 article-title: Selecting examples for partial memory learning publication-title: Mach. Learn. – start-page: 377 year: 2010 end-page: 397 ident: bib0003 publication-title: Scientific Data Mining and Knowledge Discovery – year: 2010 ident: bib0006 publication-title: Knowledge Discovery from Data Streams – start-page: 205 year: 2010 end-page: 214 ident: bib0021 article-title: Online nonstationary boosting publication-title: Proceedings of the International Workshop Multiple Classifier Systems – volume: 154 start-page: 95 year: 2004 end-page: 126 ident: bib0010 article-title: Incremental learning with partial instance memory publication-title: Artif. Intell. – volume: 3171 start-page: 286 year: 2004 end-page: 295 ident: bib0049 article-title: Learning with drift detection publication-title: Proceedings of the Brazilian Symposium on Artificial Intelligence – start-page: 1 year: 2014 end-page: 35 ident: bib0008 article-title: A survey on data stream clustering and classification publication-title: Knowl. Inf. Syst. – volume: 46 start-page: 44:1 year: 2014 end-page: 44:37 ident: bib0005 article-title: A survey on concept drift adaptation publication-title: ACM Comput. Surv. – volume: 22 start-page: 1517 year: 2011 end-page: 1531 ident: bib0028 article-title: Incremental learning of concept drift in nonstationary environments publication-title: IEEE Trans. Neural Netw. – volume: 37 start-page: 132 year: 2017 end-page: 156 ident: bib0012 article-title: Ensemble learning for data stream analysis: a survey publication-title: Inf. Fusion – year: 2007 ident: bib0007 article-title: Data streams: Models and Algorithms – volume: 24 start-page: 123 year: 1996 end-page: 140 ident: bib0014 article-title: Bagging predictors publication-title: Mach. Learn. – reference: S. Hettich, S. Bay, The UCI KDD archive, 1999, (University of California, Irvine, School of Information and Computer Sciences). – start-page: 355 year: 2010 end-page: 362 ident: bib0023 article-title: An algorithm on multi-view adaboost publication-title: Proceedings of international conference on Neural information processing: theory and algorithms – volume: 55 start-page: 119 year: 1997 end-page: 139 ident: bib0046 article-title: A decision-theoretic generalization of on-line learning and an application to boosting publication-title: J. Comput. Syst. Sci. – volume: 10 start-page: 12 year: 2015 end-page: 25 ident: bib0001 article-title: Learning in nonstationary environments: a survey publication-title: IEEE Comput. Intell. Mag. – volume: 16 start-page: 3 year: 2014 end-page: 17 ident: bib0013 article-title: A survey of multiple classifier systems as hybrid systems publication-title: Inf. Fusion – volume: 3 start-page: 245 year: 2002 end-page: 258 ident: bib0017 article-title: An experimental study on diversity for bagging and boosting with linear classifiers publication-title: Inf. Fusion – start-page: 148 year: 1996 end-page: 156 ident: bib0015 article-title: Experiments with a new boosting algorithm publication-title: Proceedings of the International Conference on Machine Learning – volume: 8 start-page: 2755 year: 2007 end-page: 2790 ident: bib0033 article-title: Dynamic weighted majority: an ensemble method for drifting concepts publication-title: J. Mach. Learn. Res. – year: 1984 ident: bib0050 publication-title: Classification and Regression Trees – start-page: 502 year: 1986 end-page: 507 ident: bib0004 article-title: Beyond incremental processing: tracking concept drift publication-title: Proceedings of the National Conference on Artificial Intelligence – start-page: 305 year: 2004 end-page: 312 ident: bib0032 article-title: Dynamic classifier selection for effective mining from noisy data streams publication-title: Proceedings of the IEEE International Conference on Data Mining – start-page: 1354 year: 2009 end-page: 1361 ident: bib0020 article-title: Online coordinate boosting publication-title: Proceedings of the IEEE International Conference on Computer Vision Workshops – start-page: 1017 year: 2003 end-page: 1024 ident: bib0022 article-title: Floatboost learning for classification publication-title: Advances in Neural Information Processing Systems – volume: 9714 start-page: 497 year: 2016 end-page: 504 ident: bib0030 article-title: A first attempt on online data stream classifier using context publication-title: International Conference on Data Mining and Big Data – start-page: 23 year: 2009 end-page: 37 ident: bib0047 article-title: Improving adaptive bagging methods for evolving data streams publication-title: Proceedings of the International Conference on Asian Conference Machine Learning – volume: 8227 start-page: 595 year: 2013 end-page: 604 ident: bib0045 article-title: A new on-line learning method for coping with recurring concepts: the adacc system publication-title: Neural Information Processing – start-page: 377 year: 2001 end-page: 382 ident: bib0034 article-title: A streaming ensemble algorithm (sea) for large-scale classification publication-title: Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD) – start-page: 359 year: 2001 end-page: 364 ident: bib0019 article-title: Experimental comparisons of online and batch versions of bagging and boosting publication-title: Proceedings of the International Conference Knowledge Discovery and Data Mining – volume: 25 start-page: 81 year: 2014 end-page: 94 ident: bib0041 article-title: Reacting to different types of concept drift: the accuracy updated ensemble algorithm publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 23 start-page: 69 year: 1996 end-page: 101 ident: bib0009 article-title: Learning in the presence of concept drift and hidden contexts publication-title: Mach. Learn. – start-page: 3421 year: 2006 end-page: 3427 ident: bib0026 article-title: An incremental learning algorithm of ensemble classifier systems publication-title: Proceedings of the International Joint Conference on Neural Networks – start-page: 282 year: 2004 end-page: 292 ident: bib0025 article-title: Fast and light boosting for adaptive mining of data streams publication-title: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining – year: 1993 ident: bib0037 publication-title: C4.5 Programs for Machine Learning – volume: 381 start-page: 46 year: 2017 end-page: 54 ident: bib0043 article-title: How to adjust an ensemble size in stream data mining? publication-title: Inf. Sci. – reference: . – reference: M. Lichman, UCI Machine Learning Repository, 2013. URL: – volume: 34 start-page: 18 year: 2005 end-page: 26 ident: bib0002 article-title: Mining data streams: a review publication-title: SIGMOD Record – start-page: 404 year: 2007 end-page: 409 ident: bib0036 article-title: Tracking recurrent concept drift in streaming data using ensemble classifiers publication-title: Proceedings of the International Conference on Machine Learning and Applications – volume: 55 start-page: 119 year: 1997 end-page: 139 ident: bib0016 article-title: A decision-theoretic generalization of on-line learning and application to boosting publication-title: J. Comput. Syst. Sci. – volume: 24 start-page: 620 year: 2013 end-page: 634 ident: bib0040 article-title: Just-in-time classifiers for recurrent concepts publication-title: IEEE Trans. Neural Netw. Learn. Syst. – volume: 41 start-page: 182 year: 2018 end-page: 194 ident: bib0031 article-title: Scarff: a scalable framework for streaming credit card fraud detection with spark publication-title: Inf. Fusion – volume: 90 start-page: 317 year: 2013 end-page: 346 ident: bib0048 article-title: On evaluating stream learning algorithms publication-title: Mach. Learn. – volume: 9714 start-page: 497 year: 2016 ident: 10.1016/j.inffus.2018.01.003_bib0030 article-title: A first attempt on online data stream classifier using context – volume: 381 start-page: 46 year: 2017 ident: 10.1016/j.inffus.2018.01.003_bib0043 article-title: How to adjust an ensemble size in stream data mining? publication-title: Inf. Sci. doi: 10.1016/j.ins.2016.10.028 – start-page: 148 year: 1996 ident: 10.1016/j.inffus.2018.01.003_bib0015 article-title: Experiments with a new boosting algorithm – ident: 10.1016/j.inffus.2018.01.003_bib0052 – volume: 16 start-page: 3 year: 2014 ident: 10.1016/j.inffus.2018.01.003_bib0013 article-title: A survey of multiple classifier systems as hybrid systems publication-title: Inf. Fusion doi: 10.1016/j.inffus.2013.04.006 – start-page: 226 year: 2003 ident: 10.1016/j.inffus.2018.01.003_bib0035 article-title: Mining concept-drifting data streams using ensemble classifiers – volume: 46 start-page: 44:1 issue: 4 year: 2014 ident: 10.1016/j.inffus.2018.01.003_bib0005 article-title: A survey on concept drift adaptation publication-title: ACM Comput. Surv. doi: 10.1145/2523813 – year: 1993 ident: 10.1016/j.inffus.2018.01.003_bib0037 – volume: 55 start-page: 119 year: 1997 ident: 10.1016/j.inffus.2018.01.003_bib0046 article-title: A decision-theoretic generalization of on-line learning and an application to boosting publication-title: J. Comput. Syst. Sci. doi: 10.1006/jcss.1997.1504 – volume: 22 start-page: 1517 issue: 10 year: 2011 ident: 10.1016/j.inffus.2018.01.003_bib0028 article-title: Incremental learning of concept drift in nonstationary environments publication-title: IEEE Trans. Neural Netw. doi: 10.1109/TNN.2011.2160459 – volume: 8 start-page: 2755 year: 2007 ident: 10.1016/j.inffus.2018.01.003_bib0033 article-title: Dynamic weighted majority: an ensemble method for drifting concepts publication-title: J. Mach. Learn. Res. – volume: 41 start-page: 182 year: 2018 ident: 10.1016/j.inffus.2018.01.003_bib0031 article-title: Scarff: a scalable framework for streaming credit card fraud detection with spark publication-title: Inf. Fusion doi: 10.1016/j.inffus.2017.09.005 – start-page: 205 year: 2010 ident: 10.1016/j.inffus.2018.01.003_bib0021 article-title: Online nonstationary boosting – year: 2007 ident: 10.1016/j.inffus.2018.01.003_bib0007 – volume: 23 start-page: 69 issue: 1 year: 1996 ident: 10.1016/j.inffus.2018.01.003_bib0009 article-title: Learning in the presence of concept drift and hidden contexts publication-title: Mach. Learn. doi: 10.1007/BF00116900 – volume: 108 start-page: 212 issue: 2 year: 1994 ident: 10.1016/j.inffus.2018.01.003_bib0044 article-title: The weighted majority algorithm publication-title: Inf. Comput. doi: 10.1006/inco.1994.1009 – year: 2010 ident: 10.1016/j.inffus.2018.01.003_bib0006 – volume: 37 start-page: 132 year: 2017 ident: 10.1016/j.inffus.2018.01.003_bib0012 article-title: Ensemble learning for data stream analysis: a survey publication-title: Inf. Fusion doi: 10.1016/j.inffus.2017.02.004 – year: 1984 ident: 10.1016/j.inffus.2018.01.003_bib0050 – start-page: 105 year: 2001 ident: 10.1016/j.inffus.2018.01.003_bib0018 article-title: Online bagging and boosting – start-page: 359 year: 2001 ident: 10.1016/j.inffus.2018.01.003_bib0019 article-title: Experimental comparisons of online and batch versions of bagging and boosting – start-page: 404 year: 2007 ident: 10.1016/j.inffus.2018.01.003_bib0036 article-title: Tracking recurrent concept drift in streaming data using ensemble classifiers – volume: 90 start-page: 317 issue: 3 year: 2013 ident: 10.1016/j.inffus.2018.01.003_bib0048 article-title: On evaluating stream learning algorithms publication-title: Mach. Learn. doi: 10.1007/s10994-012-5320-9 – volume: 6679 start-page: 155 year: 2011 ident: 10.1016/j.inffus.2018.01.003_bib0042 article-title: Accuracy updated ensemble for data streams with concept drift – start-page: 305 year: 2004 ident: 10.1016/j.inffus.2018.01.003_bib0032 article-title: Dynamic classifier selection for effective mining from noisy data streams – start-page: 377 year: 2010 ident: 10.1016/j.inffus.2018.01.003_bib0003 – start-page: 355 year: 2010 ident: 10.1016/j.inffus.2018.01.003_bib0023 article-title: An algorithm on multi-view adaboost – start-page: 1017 year: 2003 ident: 10.1016/j.inffus.2018.01.003_bib0022 article-title: Floatboost learning for classification – year: 1999 ident: 10.1016/j.inffus.2018.01.003_bib0051 article-title: Splice-2 comparative evaluation: electricity pricing – volume: 25 start-page: 81 issue: 1 year: 2014 ident: 10.1016/j.inffus.2018.01.003_bib0041 article-title: Reacting to different types of concept drift: the accuracy updated ensemble algorithm publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2013.2251352 – volume: 34 start-page: 18 issue: 2 year: 2005 ident: 10.1016/j.inffus.2018.01.003_bib0002 article-title: Mining data streams: a review publication-title: SIGMOD Record doi: 10.1145/1083784.1083789 – volume: 24 start-page: 620 issue: 4 year: 2013 ident: 10.1016/j.inffus.2018.01.003_bib0040 article-title: Just-in-time classifiers for recurrent concepts publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2013.2239309 – volume: 154 start-page: 95 year: 2004 ident: 10.1016/j.inffus.2018.01.003_bib0010 article-title: Incremental learning with partial instance memory publication-title: Artif. Intell. doi: 10.1016/j.artint.2003.04.001 – volume: 38 start-page: 43 year: 2017 ident: 10.1016/j.inffus.2018.01.003_bib0024 article-title: Multi-view learning overview: recent progress and new challenges publication-title: Inf. Fusion doi: 10.1016/j.inffus.2017.02.007 – volume: 10 start-page: 12 year: 2015 ident: 10.1016/j.inffus.2018.01.003_bib0001 article-title: Learning in nonstationary environments: a survey publication-title: IEEE Comput. Intell. Mag. doi: 10.1109/MCI.2015.2471196 – start-page: 502 year: 1986 ident: 10.1016/j.inffus.2018.01.003_bib0004 article-title: Beyond incremental processing: tracking concept drift – volume: 3171 start-page: 286 year: 2004 ident: 10.1016/j.inffus.2018.01.003_bib0049 article-title: Learning with drift detection – volume: 41 start-page: 27 year: 2000 ident: 10.1016/j.inffus.2018.01.003_bib0039 article-title: Selecting examples for partial memory learning publication-title: Mach. Learn. doi: 10.1023/A:1007661119649 – start-page: 97 year: 2001 ident: 10.1016/j.inffus.2018.01.003_bib0038 article-title: Mining time-changing data streams – start-page: 282 year: 2004 ident: 10.1016/j.inffus.2018.01.003_bib0025 article-title: Fast and light boosting for adaptive mining of data streams – volume: 3 start-page: 245 year: 2002 ident: 10.1016/j.inffus.2018.01.003_bib0017 article-title: An experimental study on diversity for bagging and boosting with linear classifiers publication-title: Inf. Fusion doi: 10.1016/S1566-2535(02)00093-3 – volume: 43 start-page: 33 year: 2018 ident: 10.1016/j.inffus.2018.01.003_bib0029 article-title: Mode tracking using multiple data streams publication-title: Inf. Fusion doi: 10.1016/j.inffus.2017.11.011 – volume: 24 start-page: 123 issue: 2 year: 1996 ident: 10.1016/j.inffus.2018.01.003_bib0014 article-title: Bagging predictors publication-title: Mach. Learn. doi: 10.1007/BF00058655 – start-page: 1 year: 2014 ident: 10.1016/j.inffus.2018.01.003_bib0008 article-title: A survey on data stream clustering and classification publication-title: Knowl. Inf. Syst. – volume: 8227 start-page: 595 year: 2013 ident: 10.1016/j.inffus.2018.01.003_bib0045 article-title: A new on-line learning method for coping with recurring concepts: the adacc system – volume: 55 start-page: 119 issue: 1 year: 1997 ident: 10.1016/j.inffus.2018.01.003_bib0016 article-title: A decision-theoretic generalization of on-line learning and application to boosting publication-title: J. Comput. Syst. Sci. doi: 10.1006/jcss.1997.1504 – start-page: 377 year: 2001 ident: 10.1016/j.inffus.2018.01.003_bib0034 article-title: A streaming ensemble algorithm (sea) for large-scale classification – start-page: 1354 year: 2009 ident: 10.1016/j.inffus.2018.01.003_bib0020 article-title: Online coordinate boosting – start-page: 3421 year: 2006 ident: 10.1016/j.inffus.2018.01.003_bib0026 article-title: An incremental learning algorithm of ensemble classifier systems – volume: 7619 start-page: 313 year: 2012 ident: 10.1016/j.inffus.2018.01.003_bib0011 article-title: Batch-incremental versus instance-incremental learning in dynamic and evolving data – volume: 11 start-page: 3 issue: 1 year: 2007 ident: 10.1016/j.inffus.2018.01.003_bib0027 article-title: Boosting classifiers for drifting concepts publication-title: Intell. Data Anal. doi: 10.3233/IDA-2007-11102 – ident: 10.1016/j.inffus.2018.01.003_bib0053 – start-page: 23 year: 2009 ident: 10.1016/j.inffus.2018.01.003_bib0047 article-title: Improving adaptive bagging methods for evolving data streams |
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