Paper Info
Title
An Interval Valued K-Nearest Neighbors Classifier
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.
Year
Venue
Keywords
2015
PROCEEDINGS OF THE 2015 CONFERENCE OF THE INTERNATIONAL FUZZY SYSTEMS ASSOCIATION AND THE EUROPEAN SOCIETY FOR FUZZY LOGIC AND TECHNOLOGY
Fuzzy Nearest Neighbor,Interval Valued Fuzzy Sets,Supervised Learning,Classification
Field
DocType
Volume
k-nearest neighbors algorithm,Fuzzy classification,Pattern recognition,Fuzzy logic,Nonparametric statistics,Fuzzy set,Supervised learning,Artificial intelligence,Classifier (linguistics),Mathematics,Machine learning,Computation
Conference
89
ISSN
Citations 
PageRank 
1951-6851
1
0.36
References 
Authors
15
4
Name
Order
Citations
PageRank
Joaquín Derrac1255264.42
Francisco Chiclana26350284.13
Salvador García34151118.45
Francisco Herrera4273911168.49