%0 Conference Proceedings
%T Identifying Features with Concept Drift in Multidimensional Data Using Statistical Tests
%+ Wroclaw University of Science and Technology
%A Sobolewski, Piotr
%A Woźniak, Michał
%Z Part 9: Feature Extraction
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Rhodes, Greece
%Y Lazaros Iliadis
%Y Ilias Maglogiannis
%Y Harris Papadopoulos
%I Springer
%3 Artificial Intelligence Applications and Innovations
%V AICT-436
%P 405-413
%8 2014-09-19
%D 2014
%R 10.1007/978-3-662-44654-6_40
%K Concept drift
%K detection
%K statistical test
%Z Computer Science [cs]Conference papers
%X Concept drift is a common problem in the data streams, which makes the classifiers no longer valid.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.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-01391341/document
%2 https://inria.hal.science/hal-01391341/file/978-3-662-44654-6_40_Chapter.pdf
%L hal-01391341
%U https://inria.hal.science/hal-01391341
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-436