An Interval Valued K-Nearest Neighbors Classifier
- https://doi.org/10.2991/ifsa-eusflat-15.2015.55How to use a DOI?
- Fuzzy nearest neighbor, interval valued fuzzy sets, supervised learning, classification.
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.
- © 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/).
Cite this article
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 - https://doi.org/10.2991/ifsa-eusflat-15.2015.55 ID - Derrac2015/06 ER -