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Using Choquet integrals for kNN approximation and classification

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conference contribution
posted on 2008-01-01, 00:00 authored by Gleb BeliakovGleb Beliakov, Simon JamesSimon James
k-nearest neighbors (kNN) is a popular method for function approximation and classification. One drawback of this method is that the nearest neighbors can be all located on one side of the point in question x. An alternative natural neighbors method is expensive for more than three variables. In this paper we propose the use of the discrete Choquet integral for combining the values of the nearest neighbors so that redundant information is canceled out. We design a fuzzy measure based on location of the nearest neighbors, which favors neighbors located all around x.

History

Pagination

1311 - 1317

Location

Hong Kong

Open access

  • Yes

Start date

2008-06-01

End date

2008-06-06

ISSN

1098-7584

ISBN-13

9781424418190

Language

eng

Notes

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Publication classification

E1 Full written paper - refereed; E Conference publication

Copyright notice

2008, IEEE.

Editor/Contributor(s)

G Feng

Title of proceedings

2008 IEEE International Conference on Fuzzy Systems : proceedings : FUZZ-IEEE 2008