{"type":"ScholarlyArticle","title":"Adaptive Ensemble Active Learning for Drifting Data Stream Mining","authors":[{"name":"Bartosz Krawczyk","slug":"bartosz-krawczyk","given":"Bartosz","family":"Krawczyk","affiliation":"Department of Computer Science , Virginia Commonwealth University , Richmond VA , USA"},{"name":"Alberto Cano","slug":"alberto-cano","given":"Alberto","family":"Cano","affiliation":"Department of Computer Science , Virginia Commonwealth University , Richmond VA , USA"}],"abstract":"Learning from data streams is among the most vital contemporary fields in machine learning and data mining. Streams pose new challenges to learning systems, due to their volume and velocity, as well as ever-changing nature caused by concept drift. Vast majority of works for data streams assume a fully supervised learning scenario, having an unrestricted access to class labels. This assumption does not hold in real-world applications, where obtaining ground truth is costly and time-consuming. Therefore, we need to carefully select which instances should be labeled, as usually we are working under a strict label budget. In this paper, we propose a novel active learning approach based on ensemble algorithms that is capable of using multiple base classifiers during the label query process. It is a plug-in solution, capable of working with most of existing streaming ensemble classifiers. We realize this process as a Multi-Armed Bandit problem, obtaining an efficient and adaptive ensemble active learning procedure by selecting the most competent classifier from the pool for each query. In order to better adapt to concept drifts, we guide our instance selection by measuring the generalization capabilities of our classifiers. This adaptive solution leads not only to better instance selection under sparse access to class labels, but also to improved adaptation to various types of concept drift and increasing the diversity of the underlying ensemble classifier.","doi":"10.24963/ijcai.2019/383","url":"https://ijcai.org/proceedings/2019/383","pdf":"https://ijcai.org/proceedings/2019/0383.pdf","markdown":"https://ijcai.org/proceedings/2019/383.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/383","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":383,"pages":{"first":2763,"last":2771},"keywords":["Machine Learning: Active Learning","Machine Learning: Time-series;Data Streams"],"videos":[],"references":[{"position":1,"raw":"[Alabdulrahman et al., 2016] Rabaa Alabdulrahman, Herna Viktor, and Eric Paquet. An active learning approach for ensemble-based data stream mining. In KDIR, pages 275- 282. SciTePress, 2016.","title":"An active learning approach for ensemble-based data stream mining","authors":"Alabdulrahman","year":"2016","venue":"KDIR","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Almeida et al., 2018] Paulo R. L. Almeida, Luiz S. Oliveira, Alceu S. Britto Jr., and Robert Sabourin. Adapting dy- namic classifier selection for concept drift. Expert Syst. Appl., 104:67-85, 2018.","title":"Adapting dynamic classifier selection for concept drift","authors":"R L Paulo, Luiz S Almeida, Alceu S Oliveira, Robert Britto, Sabourin","year":"2018","venue":"Expert Syst. Appl","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Benavoli et al., 2017] Alessio Benavoli, Giorgio Corani, Janez Demsar, and Marco Zaffalon. Time for a change: a tutorial for comparing multiple classifiers through bayesian analysis. Journal of Machine Learning Research, 18:77:1-77:36, 2017.","title":"Time for a change: a tutorial for comparing multiple classifiers through bayesian analysis","authors":"Alessio Benavoli, Giorgio Corani, Janez Demsar, Marco Zaffalon","year":"2017","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bifet et al., 2010a] Albert Bifet, Geoff Holmes, Richard Kirkby, and Bernhard Pfahringer. MOA: massive on- line analysis. Journal of Machine Learning Research, 11:1601-1604, 2010.","title":"MOA: massive online analysis","authors":"Bifet","year":"2010","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bifet et al., 2010b] Albert Bifet, Geoffrey Holmes, and Bernhard Pfahringer. Leveraging bagging for evolving data streams. In Machine Learning and Knowledge Dis- covery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20-24, 2010, Proceed- ings, Part I, pages 135-150, 2010.","title":"Leveraging bagging for evolving data streams","authors":"Bifet","year":"2010","venue":"Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Bifet et al., 2015] Albert Bifet, Gianmarco De Francisci Morales, Jesse Read, Geoff Holmes, and Bernhard Pfahringer. Efficient online evaluation of big data stream classifiers. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10-13, 2015, pages 59-68, 2015.","title":"Efficient online evaluation of big data stream classifiers","authors":"Bifet","year":"2015","venue":"Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Brown and Kuncheva, 2010] Gavin Brown and Ludmila I. Kuncheva. \"good\" and \"bad\" diversity in majority vote en- sembles. In MCS, volume 5997 of Lecture Notes in Com- puter Science, pages 124-133. Springer, 2010.","title":"good\" and \"bad\" diversity in majority vote ensembles","authors":"Gavin Brown, Ludmila I Kuncheva","year":"2010","venue":"MCS","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Brzezinski and Stefanowski, 2014] Dariusz Brzezinski and Jerzy Stefanowski. Reacting to different types of concept drift: The accuracy updated ensemble algorithm. IEEE Trans. Neural Netw. Learning Syst., 25(1):81-94, 2014.","title":"Reacting to different types of concept drift: The accuracy updated ensemble algorithm","authors":"Dariusz Brzezinski, Jerzy Stefanowski","year":"2014","venue":"IEEE Trans. Neural Netw. Learning Syst","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Brzezinski and Stefanowski, 2016] Dariusz Brzezinski and Jerzy Stefanowski. Ensemble diversity in evolving data streams. In Discovery Science -19th International Con- ference, DS 2016, Bari, Italy, October 19-21, 2016, Pro- ceedings, pages 229-244, 2016.","title":"Ensemble diversity in evolving data streams","authors":"Dariusz Brzezinski, Jerzy Stefanowski","year":"2016","venue":"Discovery Science -19th International Conference, DS 2016","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Burtini et al., 2015] Giuseppe Burtini, Jason Loeppky, and Ramon Lawrence. A survey of online experiment de- sign with the stochastic multi-armed bandit. CoRR, abs/1510.00757, 2015.","title":"A survey of online experiment design with the stochastic multi-armed bandit","authors":"Giuseppe Burtini, Jason Loeppky, Ramon Lawrence","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Czarnecki and Podolak, 2014] Wojciech M. Czarnecki and Igor T. Podolak. Adaptive active learning as a multi-armed bandit problem. In ECAI 2014 -21st European Confer- ence on Artificial Intelligence, 18-22 August 2014, Prague, Czech Republic -Including Prestigious Applications of In- telligent Systems (PAIS 2014), pages 989-990, 2014.","title":"Adaptive active learning as a multi-armed bandit problem","authors":"Wojciech M Czarnecki, Igor T Podolak","year":"2014","venue":"Czech Republic -Including Prestigious Applications of Intelligent Systems (PAIS 2014)","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Dyer et al., 2014] Karl B. Dyer, Robert Capo, and Robi Po- likar. COMPOSE: A semisupervised learning framework for initially labeled nonstationary streaming data. IEEE Trans. Neural Netw. Learning Syst., 25(1):12-26, 2014.","title":"COMPOSE: A semisupervised learning framework for initially labeled nonstationary streaming data","authors":"Karl B Dyer, Robert Capo, Robi Polikar","year":"2014","venue":"IEEE Trans. Neural Netw. Learning Syst","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Gama et al., 2013] João Gama, Raquel Sebastião, and Pe- dro Pereira Rodrigues. On evaluating stream learning al- gorithms. Machine Learning, 90(3):317-346, 2013.","title":"On evaluating stream learning algorithms","authors":"João Gama, Raquel Sebastião, Pedro Pereira Rodrigues","year":"2013","venue":"Machine Learning","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Gama et al., 2014] João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM Comput. Surv., 46(4):44:1-44:37, 2014.","title":"A survey on concept drift adaptation","authors":"João Gama, Indre Zliobaite, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia","year":"2014","venue":"ACM Comput. Surv","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Krawczyk et al., 2017] Bartosz Krawczyk, Leandro L. Minku, João Gama, Jerzy Stefanowski, and Michal Woźniak. Ensemble learning for data stream analysis: A survey. Information Fusion, 37:132-156, 2017.","title":"Ensemble learning for data stream analysis: A survey","authors":"Bartosz Krawczyk, Leandro L Minku, João Gama, Jerzy Stefanowski, Michal Woźniak","year":"2017","venue":"Information Fusion","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Krawczyk et al., 2018] Bartosz Krawczyk, Bernhard Pfahringer, and Michal Wozniak. Combining active learning with concept drift detection for data stream mining. In IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018, pages 2239-2244, 2018.","title":"Combining active learning with concept drift detection for data stream mining","authors":"Bartosz Krawczyk, Bernhard Pfahringer, Michal Wozniak","year":"2018","venue":"IEEE International Conference on Big Data, Big Data 2018","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Ksieniewicz et al., 2019] Pawel Ksieniewicz, Michal Woz- niak, Boguslaw Cyganek, Andrzej Kasprzak, and Krzysztof Walkowiak. Data stream classification using active learned neural networks. Neurocomputing, 353:74- 82, 2019.","title":"Data stream classification using active learned neural networks","authors":"Pawel Ksieniewicz, Michal Wozniak, Boguslaw Cyganek, Andrzej Kasprzak, Krzysztof Walkowiak","year":"2019","venue":"Neurocomputing","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Kuleshov and Precup, 2014] Volodymyr Kuleshov and Doina Precup. Algorithms for multi-armed bandit problems. CoRR, abs/1402.6028, 2014.","title":"Algorithms for multi-armed bandit problems","authors":"Precup Kuleshov, Volodymyr Kuleshov, Doina Precup","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lughofer, 2017] Edwin Lughofer. On-line active learning: A new paradigm to improve practical useability of data stream modeling methods. Inf. Sci., 415:356-376, 2017.","title":"On-line active learning: A new paradigm to improve practical useability of data stream modeling methods","authors":"Edwin Lughofer","year":"2017","venue":"Inf. Sci","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Minku et al., 2010] Leandro L. Minku, Allan P. White, and Xin Yao. The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans. Knowl. Data Eng., 22(5):730-742, 2010.","title":"The impact of diversity on online ensemble learning in the presence of concept drift","authors":"Leandro L Minku, Allan P White, Xin Yao","year":"2010","venue":"IEEE Trans. Knowl. Data Eng","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Mohamad et al., 2018a] Saad Mohamad, Abdelhamid Bouchachia, and Moamar Sayed Mouchaweh. A bi- criteria active learning algorithm for dynamic data streams. IEEE Trans. Neural Netw. Learning Syst., 29(1):74-86, 2018.","title":"A bicriteria active learning algorithm for dynamic data streams","authors":"Mohamad","year":"2018","venue":"IEEE Trans. Neural Netw. Learning Syst","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Mohamad et al., 2018b] Saad Mohamad, Moamar Sayed Mouchaweh, and Abdelhamid Bouchachia. Active learn- ing for classifying data streams with unknown number of classes. Neural Networks, 98:1-15, 2018.","title":"Active learning for classifying data streams with unknown number of classes","authors":"Mohamad","year":"2018","venue":"Neural Networks","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Oza and Russell, 2001] Nikunj C. Oza and Stuart J. Rus- sell. Online bagging and boosting. In Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, AISTATS 2001, Key West, Florida, USA, January 4-7, 2001, 2001.","title":"Online bagging and boosting","authors":"C Nikunj, Stuart J Oza, Russell","year":"2001","venue":"Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, AISTATS 2001","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Xu et al., 2016] Wenhua Xu, Fengfei Zhao, and Zhengcai Lu. Active learning over evolving data streams using paired ensemble framework. In ICACI, pages 180-185. IEEE, 2016.","title":"Active learning over evolving data streams using paired ensemble framework","authors":"Wenhua Xu, Fengfei Zhao, Zhengcai Lu","year":"2016","venue":"ICACI","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Zliobaite et al., 2014] Indre Zliobaite, Albert Bifet, Bern- hard Pfahringer, and Geoffrey Holmes. Active learning with drifting streaming data. IEEE Trans. Neural Netw. Learning Syst., 25(1):27-39, 2014.","title":"Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, and Geoffrey Holmes","authors":"Zliobaite","year":"2014","venue":"IEEE Trans. Neural Netw. Learning Syst","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zyblewski et al., 2019] Pawel Zyblewski, Pawel Ksie- niewicz, and Michal Wozniak. Classifier selection for highly imbalanced data streams with minority driven en- semble. In Artificial Intelligence and Soft Computing - 18th International Conference, ICAISC 2019, Zakopane, Poland, June 16-20, 2019, Proceedings, Part I, pages 626- 635, 2019.","title":"Classifier selection for highly imbalanced data streams with minority driven ensemble","authors":"Pawel Zyblewski, Pawel Ksieniewicz, Michal Wozniak","year":"2019","venue":"Artificial Intelligence and Soft Computing -18th International Conference, ICAISC 2019","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}