An Interval Valued K-Nearest Neighbors Classifier

DOI

10.2991/ifsa-eusflat-15.2015.55How to use a DOI?

Keywords

Fuzzy nearest neighbor, interval valued fuzzy sets, supervised learning, classification.

Abstract

The K-Nearest Neighbors (k-NN) classifier has become a well-known, successful method for pattern classification tasks. In recent years, many enhancements to the original algorithm have been proposed. Fuzzy sets theory has been the basis of several proposed models towards the enhancement of the nearest neighbors rule, being the Fuzzy K-Nearest Neighbors (FuzzyKNN) classifier the most notable procedure in the field. In this work we present a new approach to the nearest neighbor classifier based on the use of interval valued fuzzy sets. The use and implementation of interval values facilitates the membership of the instances and the computation of the votes in a more flexible way than the original FuzzyKNN method, thus improving its adaptability to different supervised learning problems. An experimental study, contrasted by the application of nonparametric statistical procedures, is carried out to ascertain whether the Interval Valued K-Nearest Neighbor (IV-KNN) classifier proposed here is significantly more accurate than k-NN, FuzzyKNN and other fuzzy nearest neighbor classifiers. We conclude that the IV-KNN is indeed significantly more accurate than the rest of classifiers analyzed.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Joaquín Derrac
AU  - Francisco Chiclana
AU  - Salvador García
AU  - Francisco Herrera
PY  - 2015/06
DA  - 2015/06
TI  - An Interval Valued K-Nearest Neighbors Classifier
BT  - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology
PB  - Atlantis Press
SP  - 378
EP  - 384
SN  - 1951-6851
UR  - https://doi.org/10.2991/ifsa-eusflat-15.2015.55
DO  - 10.2991/ifsa-eusflat-15.2015.55
ID  - Derrac2015/06
ER  -