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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2012: Artificial Intelligence and Soft Computing pp 378–386Cite as

Bandwidth Selection in Kernel Density Estimators for Multiple-Resolution Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7267))

Abstract

We consider a problem of selection of parameters in a classifier based on the average of kernel density estimators where each estimator corresponds to a different data “resolution”. The selection is based on adjusting parameters of the estimators to minimize a substitute of the misclassification ratio. We experimentally compare the misclassification ratio and parameters selected for benchmark data sets by the introduced algorithm with these values of the algorithm’s baseline version. In order to place the classification results in a wider context, we compare them with results of other popular classifiers.

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Author information

Authors and Affiliations

  1. Faculty of Mathematics and Information Science, Warsaw University of Technology, Plac Politechniki 1, 00-661, Warsaw, Poland

    Mateusz Kobos & Jacek Mańdziuk

Authors
  1. Mateusz Kobos
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  2. Jacek Mańdziuk
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Kobos, M., Mańdziuk, J. (2012). Bandwidth Selection in Kernel Density Estimators for Multiple-Resolution Classification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_44

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  • DOI: https://doi.org/10.1007/978-3-642-29347-4_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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