Universität zu Köln

Pokotylo, Oleksii and Mosler, Karl (2019). Classification with the pot-pot plot. Stat. Pap., 60 (3). S. 553 - 582. NEW YORK: SPRINGER. ISSN 1613-9798

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Abstract

We propose a procedure for supervised classification that is based on potential functions. The potential of a class is defined as a kernel density estimate multiplied by the class's prior probability. The method transforms the data to a potential-potential (pot-pot) plot, where each data point is mapped to a vector of potentials. Separation of the classes, as well as classification of new data points, is performed on this plot. For this, either the -procedure (-P) or k-nearest neighbors (k-NN) are employed. For data that are generated from continuous distributions, these classifiers prove to be strongly Bayes-consistent. The potentials depend on the kernel and its bandwidth used in the density estimate. We investigate several variants of bandwidth selection, including joint and separate pre-scaling and a bandwidth regression approach. The new method is applied to benchmark data from the literature, including simulated data sets as well as 50 sets of real data. It compares favorably to known classification methods such as LDA, QDA, max kernel density estimates, k-NN, and DD-plot classification using depth functions.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Pokotylo, Oleksii UNSPECIFIED UNSPECIFIED UNSPECIFIED Mosler, Karl UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-147563
DOI:	10.1007/s00362-016-0854-8
Journal or Publication Title:	Stat. Pap.
Volume:	60
Number:	3
Page Range:	S. 553 - 582
Date:	2019
Publisher:	SPRINGER
Place of Publication:	NEW YORK
ISSN:	1613-9798
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Statistics & Probability Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/14756