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.
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Sobolewski, P., Woźniak, M. (2012). Data with Shifting Concept Classification Using Simulated Recurrence. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Intelligent Information and Database Systems. ACIIDS 2012. Lecture Notes in Computer Science(), vol 7196. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28487-8_42
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DOI: https://doi.org/10.1007/978-3-642-28487-8_42
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