{"type":"ScholarlyArticle","title":"Self-Adjusting Memory: How to Deal with Diverse Drift Types","authors":[{"name":"Viktor Losing","slug":"viktor-losing","given":"Viktor","family":"Losing","affiliation":"Bielefeld University Universitätsstr. 25 , 33615 Bielefeld"},{"name":"Barbara Hammer","slug":"barbara-hammer","given":"Barbara","family":"Hammer","affiliation":"Bielefeld University Universitätsstr. 25 , 33615 Bielefeld"},{"name":"Heiko Wersing","slug":"heiko-wersing","given":"Heiko","family":"Wersing","affiliation":"HONDA Research Institute Europe Carl-Legien-Str. 30 , 63073 Offenbach"}],"abstract":"Data Mining in non-stationary data streams is particularly relevant  in the context of the Internet of Things and Big Data. Its challenges arise from fundamentally different drift types violating assumptions of data independence or stationarity. Available methods often struggle with certain forms of drift or require unavailable a priori task knowledge. We propose the Self-Adjusting Memory (SAM) model for the k Nearest Neighbor (kNN) algorithm. SAM-kNN can deal with heterogeneous concept drift, i.e. different drift types and rates. Its basic idea are dedicated models for current and former concepts used according to the demands of the given situation. It can be robustly applied in practice without meta parameter optimization.\r\nWe conduct an extensive evaluation on various benchmarks, consisting of artificial streams with known drift characteristics and real-world datasets. Highly competitive results throughout all experiments underline the robustness of SAM-kNN as well as its capability to handle heterogeneous concept drift.","doi":"10.24963/ijcai.2017/690","url":"https://ijcai.org/proceedings/2017/690","pdf":"https://ijcai.org/proceedings/2017/0690.pdf","markdown":"https://ijcai.org/proceedings/2017/690.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/690","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":"Best Sister Conferences","track":"Best Sister Conferences","number":690,"pages":{"first":4899,"last":4903},"keywords":["Artificial Intelligence: machine learning","Artificial Intelligence: artificial intelligence","Artificial Intelligence: uncertainty in artificial intelligence","Artificial Intelligence: other"],"videos":[],"references":[{"position":1,"raw":"[Arthur and Vassilvitskii, 2007] David Arthur and Sergei Vassilvitskii. k-means++: The Advantages of Careful Seeding. In Proceedings of the eighteenth annual ACM- SIAM symposium on Discrete algorithms, pages 1027- 1035. Society for Industrial and Applied Mathematics, 2007.","title":"David Arthur and Sergei Vassilvitskii. k-means++: The Advantages of Careful Seeding","authors":"Vassilvitskii Arthur","year":"2007","venue":"Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Atkinson and Shiffrin, 1968] Richard C Atkinson and Richard M Shiffrin. Human memory: A proposed system and its control processes. The psychology of learning and motivation, 2:89-195, 1968.","title":"Human memory: A proposed system and its control processes","authors":"C Richard, Richard M Atkinson, Shiffrin","year":"1968","venue":"The psychology of learning and motivation","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Atzori et al., 2010] Luigi Atzori, Antonio Iera, and Gia- como Morabito. The Internet of Things: A Survey. Com- puter networks, 54(15):2787-2805, 2010.","title":"The Internet of Things: A Survey","authors":"Atzori","year":"2010","venue":"Computer networks","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Bifet and Gavalda, 2007] Albert Bifet and Ricard Gavalda. Learning from Time-Changing Data with Adaptive Win- dowing. In SIAM International Conference on Data Min- ing (SDM), volume 7, page 2007. SIAM, 2007.","title":"Albert Bifet and Ricard Gavalda. Learning from Time-Changing Data with Adaptive Windowing","authors":"Gavalda Bifet","year":"2007","venue":"SIAM International Conference on Data Mining (SDM)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Bifet et al., 2010] Albert Bifet, Geoff Holmes, and Bern- hard Pfahringer. Leveraging Bagging for Evolving Data Streams. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 135-150. Springer, 2010.","title":"Albert Bifet, Geoff Holmes, and Bernhard Pfahringer. Leveraging Bagging for Evolving Data Streams","authors":"Bifet","year":"2010","venue":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Bifet et al., 2013] Albert Bifet, Bernhard Pfahringer, Jesse Read, and Geoff Holmes. Efficient Data Stream Classifica- tion via Probabilistic Adaptive Windows. In Proceedings of the 28th Annual ACM Symposium on Applied Comput- ing, SAC '13, pages 801-806, New York, NY, USA, 2013. ACM.","title":"Efficient Data Stream Classification via Probabilistic Adaptive Windows","authors":"Bifet","year":"2013","venue":"Proceedings of the 28th Annual ACM Symposium on Applied Computing, SAC '13","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Chen et al., 2014] M. Chen, S. Mao, and Y. Liu. Big data: A survey. Mobile Netw. and Appl., 19(2), 2014.","title":"Big data: A survey","authors":"M Chen, S Mao, Y Liu","year":"2014","venue":"Mobile Netw. and Appl","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Ditzler et al., 2015] Gregory Ditzler, Manuel Roveri, Ce- sare Alippi, and Robi Polikar. Learning in nonstationary environments: A survey. Computational Intelligence Mag- azine, IEEE, 10(4):12-25, 2015.","title":"Learning in nonstationary environments: A survey","authors":"Gregory Ditzler, Manuel Roveri, Cesare Alippi, Robi Polikar","year":"2015","venue":"Computational Intelligence Magazine","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Elwell and Polikar, 2011] R. Elwell and R. Polikar. In- cremental learning of concept drift in nonstationary en- vironments. IEEE Transactions on Neural Networks, 22(10):1517-1531, Oct 2011.","title":"Incremental learning of concept drift in nonstationary environments","authors":"R Elwell, R Polikar","year":"2011","venue":"IEEE Transactions on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Gama et al., 2014] João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, and Abdelhamid Bouchachia. A survey on concept drift adaptation. ACM Computing Surveys (CSUR), 46(4):44, 2014.","title":"A survey on concept drift adaptation","authors":"João Gama, Indrė Žliobaitė, Albert Bifet, Mykola Pechenizkiy, Abdelhamid Bouchachia","year":"2014","venue":"ACM Computing Surveys (CSUR)","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jaber et al., 2013] Ghazal Jaber, Antoine Cornuéjols, and Philippe Tarroux. Online learning: Searching for the best forgetting strategy under concept drift. In Neural Informa- tion Processing, pages 400-408. Springer, 2013.","title":"Online learning: Searching for the best forgetting strategy under concept drift","authors":"Ghazal Jaber, Antoine Cornuéjols, Philippe Tarroux","year":"2013","venue":"Neural Information Processing","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Kolter and Maloof, 2007] J Zico Kolter and Marcus A Mal- oof. Dynamic weighted majority: An ensemble method for drifting concepts. The Journal of Machine Learning Research, 8:2755-2790, 2007.","title":"Dynamic weighted majority: An ensemble method for drifting concepts","authors":"Maloof ; J Zico Kolter, Marcus A Kolter, Maloof","year":"2007","venue":"The Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Losing et al., 2016] V. Losing, B. Hammer, and H. Wers- ing. Knn classifier with self adjusting memory for hetero- geneous concept drift. In 2016 IEEE 16th International Conference on Data Mining (ICDM), pages 291-300, Dec 2016.","title":"Knn classifier with self adjusting memory for heterogeneous concept drift","authors":"V Losing, B Hammer, H Wersing","year":"2016","venue":"IEEE 16th International Conference on Data Mining (ICDM)","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Losing et al., 2017] Viktor Losing, Barbara Hammer, and Heiko Wersing. Tackling Heterogeneous Concept Drift with the Self Adjusting Memory (SAM). Knowledge and Information Systems (under review), 2017.","title":"Viktor Losing","authors":"Losing","year":"2017","venue":"Tackling Heterogeneous Concept Drift with the Self Adjusting Memory (SAM)","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Oza, 2005] Nikunj C. Oza. Online Bagging and Boost- ing. In IEEE International Conference on Systems, Man and Cybernetics 2005, volume 3, pages 2340-2345. IEEE, 2005.","title":"Online Bagging and Boosting","authors":"C Nikunj, Oza","year":"2005","venue":"IEEE International Conference on Systems, Man and Cybernetics 2005","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Schiaffino et al., 2008] Silvia Schiaffino, Patricio Garcia, and Analia Amandi. eTeacher: Providing personalized as- sistance to e-learning students. Computers & Education, 51(4):1744-1754, 2008.","title":"eTeacher: Providing personalized assistance to e-learning students","authors":"Silvia Schiaffino, Patricio Garcia, Analia Amandi","year":"2008","venue":"Computers & Education","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}