Abstract : Concept drift is a common problem in the data streams, which makes the classifiers no longer valid. In the multidimensional data, this problem becomes difficult to tackle. This paper examines the possibilities of identifying the specific features, in which concept drift occurs. This allows to limit the scope of the necessary update in the classification system. As a tool, we select a popular Kolmogorov-Smirnov test statistic.
Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.405-413, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_40〉
https://hal.inria.fr/hal-01391341
Contributeur : Hal Ifip
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Soumis le : jeudi 3 novembre 2016 - 11:02:55
Dernière modification le : vendredi 1 décembre 2017 - 01:16:36
Document(s) archivé(s) le : samedi 4 février 2017 - 13:16:01
Piotr Sobolewski, Michał Woźniak. Identifying Features with Concept Drift in Multidimensional Data Using Statistical Tests. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-436, pp.405-413, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44654-6_40〉. 〈hal-01391341〉
