caa a22 4500 605469326 CHVBK 20210128100321.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s00500-014-1492-5 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1492-5 One-class classifiers with incremental learning and forgetting for data streams with concept drift [Elektronische Daten] [Bartosz Krawczyk, Michał Woźniak] One of the most important challenges for machine learning community is to develop efficient classifiers which are able to cope with data streams, especially with the presence of the so-called concept drift. This phenomenon is responsible for the change of classification task characteristics, and poses a challenge for the learning model to adapt itself to the current state of the environment. So there is a strong belief that one-class classification is a promising research direction for data stream analysis—it can be used for binary classification without an access to counterexamples, decomposing a multi-class data stream, outlier detection or novel class recognition. This paper reports a novel modification of weighted one-class support vector machine, adapted to the non-stationary streaming data analysis. Our proposition can deal with the gradual concept drift, as the introduced one-class classifier model can adapt its decision boundary to new, incoming data and additionally employs a forgetting mechanism which boosts the ability of the classifier to follow the model changes. In this work, we propose several different strategies for incremental learning and forgetting, and additionally we evaluate them on the basis of several real data streams. Obtained results confirmed the usability of proposed classifier to the problem of data stream classification with the presence of concept drift. Additionally, implemented forgetting mechanism assures the limited memory consumption, because only quite new and valuable examples should be memorized. The Author(s), 2014 Pattern classification nationallicence One-class classification nationallicence Data stream classification nationallicence Concept drift nationallicence Incremental learning nationallicence Forgetting nationallicence Krawczyk Bartosz Department of Systems and Computer Networks, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland aut Woźniak Michał Department of Systems and Computer Networks, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland aut Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3387-3400 1432-7643 19:12<3387 2015 19 500 https://doi.org/10.1007/s00500-014-1492-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1492-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Krawczyk Bartosz Department of Systems and Computer Networks, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland aut NATIONALLICENCE 700 1- Woźniak Michał Department of Systems and Computer Networks, Wrocław University of Technology, Wybrzeże Wyspiańskiego 27, 50-370, Wrocław, Poland aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/12(2015-12-01), 3387-3400 1432-7643 19:12<3387 2015 19 500