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Combination of independent kernel density estimators in classification

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1 Author(s)
Kobos, M. ; Fac. of Math. & Inf. Sci., Warsaw Univ. of Technol., Warsaw, Poland

A new classification algorithm based on combination of two independent kernel density estimators per class is proposed. Each estimator is characterized by a different bandwidth parameter. Combination of the estimators corresponds to viewing the data with different ¿resolutions¿. The intuition behind the method is that combining different views on the data yields a better insight into the data structure; therefore, it leads to a better classification result. The bandwidth parameters are adjusted automatically by the L-BFGS-B algorithm to minimize the cross-validation classification error. Results of experiments on benchmark data sets confirm the algorithm's applicability.

Published in:

Computer Science and Information Technology, 2009. IMCSIT '09. International Multiconference on

Date of Conference:

12-14 Oct. 2009