Abstract
Cross-entropy clustering (CEC) is a density model based clustering algorithm. In this paper we apply CEC to the one-class classification, which has several advantages over classical approaches based on Expectation Maximization (EM) and Support Vector Machines (SVM). More precisely, our model allows the use of various types of gaussian models with low computational complexity. We test the designed method on real data coming from the monitoring systems of wind turbines.
The paper was supported by the National Centre for Research and Development under Grant no. WND-DEM-1-153/01.
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Spurek, P., Wójcik, M., Tabor, J. (2015). Cross-Entropy Clustering Approach to One-Class Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2015. Lecture Notes in Computer Science(), vol 9119. Springer, Cham. https://doi.org/10.1007/978-3-319-19324-3_43
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DOI: https://doi.org/10.1007/978-3-319-19324-3_43
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