Multiple-resolution classification with combination of density estimators
We introduce a classification algorithm based on an idea of ‘multiple-resolution’ (or ‘multiscale’) approach to analysis of the data. In practice, the method uses an average of kernel density estimators where each estimator corresponds to a different data ‘resolution’.
First, we examine theoretical properties of this method; next, we propose a practical implementation of such an algorithm with parameters of density estimators adjusted to minimise the misclassification probability. Subsequently, we test the algorithm on artificial data sets characterised
by a ‘multiple-resolution’ property. The tests show that the introduced algorithm is superior to the basic version based on one estimator per class. We also test the algorithm on benchmark data sets and compare the results obtained with the results of other classification algorithms.
Keywords: average of density estimators; classification; kernel density estimator; multiple-resolution; multiscale
Document Type: Research Article
Affiliations: Faculty of Mathematics and Information Science,Warsaw University of Technology, Plac Politechniki 100-661Warsaw, Poland
Publication date: 01 December 2011
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