Data with shifting concept classification using simulated recurrence

Authors:
Piotr Sobolewski;Michał Woźniak
Affiliations:
Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Wroclaw, Poland;Department of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Wroclaw, Poland
Venue:
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part I
Year:
2012

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Abstract

One of the serious problems of modern pattern recognition is concept drift i.e., model changing during exploitation of a given classifier. The paper proposes how to adapt a single classifier system to the new model without the knowledge of correct classes. The proposed simulated concept recurrence is implemented in the non-recurring concept shift scenario without the drift detection mechanism. We assume that the model could change slightly, what allows us to predict a set of possible models. Quality of the proposed algorithm was estimated on the basis of computer experiment which was carried out on the benchmark dataset.