Bandwidth selection in kernel density estimators for multiple-resolution classification

Authors:
Mateusz Kobos;Jacek Mańdziuk
Affiliations:
Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland;Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
Venue:
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Year:
2012

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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.