{"type":"ScholarlyArticle","title":"Regional Concept Drift Detection and Density Synchronized Drift Adaptation","authors":[{"name":"Anjin Liu","slug":"anjin-liu","given":"Anjin","family":"Liu","affiliation":"Centre for Artificial Intelligence , School of Software Faculty of Engineering and Information Technology , University of Technology , Sydney , Australia"},{"name":"Yiliao Song","slug":"yiliao-song","given":"Yiliao","family":"Song","affiliation":"Centre for Artificial Intelligence , School of Software Faculty of Engineering and Information Technology , University of Technology , Sydney , Australia"},{"name":"Guangquan Zhang","slug":"guangquan-zhang","given":"Guangquan","family":"Zhang","affiliation":"Centre for Artificial Intelligence , School of Software Faculty of Engineering and Information Technology , University of Technology , Sydney , Australia"},{"name":"Jie Lu","slug":"jie-lu","given":"Jie","family":"Lu","affiliation":"Centre for Artificial Intelligence , School of Software Faculty of Engineering and Information Technology , University of Technology , Sydney , Australia"}],"abstract":"In data stream mining, the emergence of new patterns or a pattern ceasing to exist is called concept drift. Concept drift makes the learning process complicated because of the inconsistency between existing data and upcoming data. Since concept drift was first proposed, numerous articles have been published to address this issue in terms of distribution analysis. However, most distribution-based drift detection methods assume that a drift happens at an exact time point, and the data arrived before that time point is considered not important. Thus, if a drift only occurs in a small region of the entire feature space, the other non-drifted regions may also be suspended, thereby reducing the learning efficiency of models. To retrieve non-drifted information from suspended historical data, we propose a local drift degree (LDD) measurement that can continuously monitor regional density changes. Instead of suspending all historical data after a drift, we synchronize the regional density discrepancies according to LDD. Experimental evaluations on three public data sets show that our concept drift adaptation algorithm improves accuracy compared to other methods.","doi":"10.24963/ijcai.2017/317","url":"https://ijcai.org/proceedings/2017/317","pdf":"https://ijcai.org/proceedings/2017/0317.pdf","markdown":"https://ijcai.org/proceedings/2017/317.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/317","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning A-R","track":"Main track","number":317,"pages":{"first":2280,"last":2286},"keywords":["Machine Learning: Time-series/Data Streams","Machine Learning: Online Learning","Machine Learning: Classification"],"videos":[],"references":[{"position":1,"raw":"[Bifet and Gavaldà, 2009] Albert Bifet and Ricard Gavaldà, \"Adaptive learning from evolving data streams,\" in Proceedings of the Eighth International Symposium on Intelligent Data Analysis, pp. 249-260: Springer, 2009.","title":"Albert Bifet and Ricard Gavaldà, \"Adaptive learning from evolving data streams","authors":"Gavaldà Bifet","year":"2009","venue":"Proceedings of the Eighth International Symposium on Intelligent Data Analysis","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bifet, et al., 2010] Albert Bifet, Geoff Holmes, Richard Kirkby, and Bernhard Pfahringer, \"MOA: Massive online analysis,\" The Journal of Machine Learning Research, vol. 99, pp. 1601-1604, 2010.","title":"MOA: Massive online analysis","authors":"Bifet","year":"2010","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Brzeziński and Stefanowski, 2011] Dariusz Brzeziński and Jerzy Stefanowski, \"Accuracy updated ensemble for data streams with concept drift,\" in International Conference on Hybrid Artificial Intelligence Systems, pp. 155-163: Springer, 2011.","title":"Accuracy updated ensemble for data streams with concept drift","authors":"Dariusz Brzeziński, Jerzy Stefanowski","year":"2011","venue":"International Conference on Hybrid Artificial Intelligence Systems","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Elwell and Polikar, 2011] Ryan Elwell and Robi Polikar, \"Incremental learning of concept drift in nonstationary environments,\" (in eng), IEEE Transactions on Neural Networks, Research Support, U.S. Gov't, Non-P.H.S. vol. 22, no. 10, pp. 1517-31, Oct 2011.","title":"Incremental learning of concept drift in nonstationary environments","authors":"Ryan Elwell, Robi Polikar","year":"2011","venue":"IEEE Transactions on Neural Networks, Research Support, U.S. Gov't, Non-P.H.S","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Frias-Blanco, et al., 2015] Isvani Frias-Blanco, Jose del Campo-Avila, Gonzalo Ramos-Jimenes, Rafael Morales- Bueno, Agustin Ortiz-Diaz, and Yaile Caballero-Mota, \"Online and Non-Parametric Drift Detection Methods Based on Hoeffding's Bounds,\" IEEE Transactions on Knowledge and Data Engineering, vol. 27, no. 3, pp. 810- 823, 2015.","title":"Isvani Frias-Blanco, Jose del Campo-Avila, Gonzalo Ramos-Jimenes, Rafael Morales-Bueno, Agustin Ortiz-Diaz, and Yaile Caballero-Mota","authors":"Frias-Blanco","year":"2015","venue":"IEEE Transactions on Knowledge and Data Engineering","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Gama and Castillo, 2006] João Gama and Gladys Castillo, \"Learning with local drift detection,\" in International Conference on Advanced Data Mining and Applications, pp. 42-55: Springer, 2006.","title":"Learning with local drift detection","authors":"Castillo Gama","year":"2006","venue":"International Conference on Advanced Data Mining and Applications","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gama, et al., 2014] João Gama, Indrė Zliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia, \"A survey on concept drift adaptation,\" ACM Computing Surveys, vol. 46, no. 4, pp. 1-37, 2014.","title":"A survey on concept drift adaptation","authors":"João Gama, Indrė Zliobaitė, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia","year":"2014","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Harel, et al., 2014] Maayan Harel, Shie Mannor, Ran El- Yaniv, and Koby Crammer, \"Concept drift detection through resampling,\" in Proceedings of the Thirty-first International Conference on Machine Learning, pp. 1009-1017, 2014.","title":"Concept drift detection through resampling","authors":"Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer","year":"2014","venue":"Proceedings of the Thirty-first International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Ikonomovska and Gama, 2008] Elena Ikonomovska and Joao Gama, \"Learning Model Trees from Data Streams,\" in Proceedings of the Eleventh International Conference on Discovery Science, pp. 52-63, Berlin: Springer, 2008.","title":"Learning Model Trees from Data Streams","authors":"Gama Ikonomovska","year":"2008","venue":"Proceedings of the Eleventh International Conference on Discovery Science","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Ikonomovska, et al., 2011] Elena Ikonomovska, João Gama, and Sašo Džeroski, \"Learning model trees from evolving data streams,\" Data Mining and Knowledge Discovery, vol. 23, no. 1, pp. 128-168, 2011.","title":"Learning model trees from evolving data streams","authors":"Elena Ikonomovska, João Gama, Sašo Džeroski","year":"2011","venue":"Data Mining and Knowledge Discovery","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ikonomovska, et al., 2009] Elena Ikonomovska, João Gama, Raquel Sebastião, and Dejan Gjorgjevik, \"Regression Trees from Data Streams with Drift Detection,\" in Proceedings of the Twelfth International Conference on Discovery Science, pp. 121-135, Berlin: Springer, 2009.","title":"Regression Trees from Data Streams with Drift Detection","authors":"Elena Ikonomovska, João Gama, Raquel Sebastião, Dejan Gjorgjevik","year":"2009","venue":"Proceedings of the Twelfth International Conference on Discovery Science","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Katakis, et al., 2009] Ioannis Katakis, Grigorios Tsoumakas, Evangelos Banos, Nick Bassiliades, and Ioannis Vlahavas, \"An adaptive personalized news dissemination system,\" Journal of Intelligent Information Systems, vol. 32, no. 2, pp. 191-212, 2009.","title":"An adaptive personalized news dissemination system","authors":"Ioannis Katakis, Grigorios Tsoumakas, Evangelos Banos, Nick Bassiliades, Ioannis Vlahavas","year":"2009","venue":"Journal of Intelligent Information Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li, et al., 2016] Peipei Li, Lu He, Xuegang Hu, Yuhong Zhang, Lei Li, and Xindong Wu, \"Concept based short text stream classification with topic drifting detection,\" in Proceedings of the Sixteenth International Conference on Data Mining, pp. 1009-1014, 2016.","title":"Concept based short text stream classification with topic drifting detection","authors":"Peipei Li, Lu He, Xuegang Hu, Yuhong Zhang, Lei Li, Xindong Wu","year":"2016","venue":"Proceedings of the Sixteenth International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu, et al., 2017] Anjin Liu, Guangquan Zhang, and Jie Lu, \"Fuzzy time windowing for gradual concept drift adaptation,\" in Proceedings of the Twenty-sixth IEEE International Conference on Fuzzy Systems, Naples Italy: IEEE, 2017.","title":"Fuzzy time windowing for gradual concept drift adaptation","authors":"Anjin Liu, Guangquan Zhang, Jie Lu","year":"2017","venue":"Proceedings of the Twenty-sixth IEEE International Conference on Fuzzy Systems","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Losing, et al., 2016] Viktor Losing, Barbara Hammer, and Heiko Wersing, \"KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift,\" in Proceedings of the Sixteenth IEEE International Conference on Data Mining, 2016.","title":"KNN Classifier with Self Adjusting Memory for Heterogeneous Concept Drift","authors":"Losing","year":"2016","venue":"Proceedings of the Sixteenth IEEE International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Lu, et al., 2016] Ning Lu, Jie Lu, Guangquan Zhang, and Ramon Lopez De Mantaras, \"A concept drift-tolerant case-base editing technique,\" Artificial Intelligence, vol. 230, pp. 108-133, 2016.","title":"A concept drift-tolerant case-base editing technique","authors":"Ning Lu, Jie Lu, Guangquan Zhang, Ramon Lopez, De Mantaras","year":"2016","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Lu, et al., 2014] Ning Lu, Guangquan Zhang, and Jie Lu, \"Concept drift detection via competence models,\" Artificial Intelligence, vol. 209, pp. 11-28, 2014.","title":"Concept drift detection via competence models","authors":"Ning Lu, Guangquan Zhang, Jie Lu","year":"2014","venue":"Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Ross, et al., 2012] Gordon J Ross, Niall M Adams, Dimitris K Tasoulis, and David J Hand, \"Exponentially weighted moving average charts for detecting concept drift,\" Pattern Recognition Letters, vol. 33, no. 2, pp. 191-198, 2012.","title":"Exponentially weighted moving average charts for detecting concept drift","authors":"Niall M Gordon J Ross, Dimitris K Adams, David J Tasoulis, Hand","year":"2012","venue":"Pattern Recognition Letters","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Sarnelle, et al., 2015] Joseph Sarnelle, Anthony Sanchez, RRobert Capo, Joshua Haas, and Robi Polikar, \"Quantifying the limited and gradual concept drift assumption,\" in The Proceedings of 2015 International Joint Conference on Neural Networks, pp. 1-8, 2015.","title":"Quantifying the limited and gradual concept drift assumption","authors":"Joseph Sarnelle, Anthony Sanchez, Rrobert Capo, Joshua Haas, Robi Polikar","year":"2015","venue":"The Proceedings of 2015 International Joint Conference on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Shao, et al., 2014] Junming Shao, Zahra Ahmadi, and Stefan Kramer, \"Prototype-based learning on concept- drifting data streams,\" in Proceedings of the Twentieth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 412- 421, 2623609: ACM, 2014.","title":"Prototype-based learning on conceptdrifting data streams","authors":"Junming Shao, Zahra Ahmadi, Stefan Kramer","year":"2014","venue":"Proceedings of the Twentieth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Su, et al., 2008] Bai Su, Yi-Dong Shen, and Wei Xu, \"Modeling concept drift from the perspective of classifiers,\" in Proceedings of IEEE Conference on Cybernetics and Intelligent Systems, Chengdu, China, pp. 1055-1060: IEEE, 2008.","title":"Modeling concept drift from the perspective of classifiers","authors":"Bai Su, Yi-Dong Shen, Wei Xu","year":"2008","venue":"Proceedings of IEEE Conference on Cybernetics and Intelligent Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Sugiyama and Kawanabe, 2012] Masashi Sugiyama and Motoaki Kawanabe, Machine learning in non-stationary environments: Introduction to covariate shift adaptation. MIT Press, 2012.","title":"Machine learning in non-stationary environments: Introduction to covariate shift adaptation","authors":"Kawanabe Sugiyama, Masashi Sugiyama, Motoaki Kawanabe","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zliobaite, et al., 2014] Indre Zliobaite, Albert Bifet, Bernhard Pfahringer, and Geoffrey Holmes, \"Active Learning with drifting streaming data,\" IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 1, pp. 27-39, 2014.","title":"Active Learning with drifting streaming data","authors":"Zliobaite","year":"2014","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}