Abstract
The traditional classifier cannot keep its quality, when the concept drift appears. The paper proposes how to protect against classification quality decreasing when concept drift occurs. Invented methods do not train classifiers all the time but they try to use earlier gained knowledge about models and switched older model to suitable new one. In this work we assume that the set of models is known and stored as the pool of classifiers. Then, by using drift detecting and searching models methods, we can choose the best model. Our propositions and the main characteristics of them were evaluated on the basis of the experiments which were carried out on chosen artificial data set.
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Cal, P., Woźniak, M. (2012). Drift Detection and Model Selection Algorithms: Concept and Experimental Evaluation. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28931-6_53
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DOI: https://doi.org/10.1007/978-3-642-28931-6_53
Publisher Name: Springer, Berlin, Heidelberg
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