Classification based on multiple-resolution data view

Authors:
Mateusz Kobos;Jacek Mańdziuk
Affiliations:
Warsaw University of Technology, Faculty of Mathematics and Information Science, Warsaw, Poland;Warsaw University of Technology, Faculty of Mathematics and Information Science, Warsaw, Poland
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
Year:
2010

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Abstract

We examine efficacy of a classifier based on average of kernel density estimators; each estimator corresponds to a different data "resolution". Parameters of the estimators are adjusted to minimize the classification error. We propose properties of the data for which our algorithm should yield better results than the basic version of the method. Next, we generate data with postulated properties and conduct numerical experiments. Analysis of the results shows potential advantage of the new algorithm when compared with the baseline classifier.