Evolutionary fuzzy k-nearest neighbors algorithm using interval-valued fuzzy sets

One of the most known and effective methods in supervised classification is the K-Nearest Neighbors classifier. Several approaches have been proposed to enhance its precision, with the Fuzzy K-Nearest Neighbors (Fuzzy-kNN) classifier being among the most successful ones. However, despite its good behavior, Fuzzy-kNN lacks of a method for properly defining several mechanisms regarding the representation of the relationship between the instances and the classes of the classification problems. Such a method would be very desirable, since it would potentially lead to an improvement in the precision of the classifier. In this work we present a new approach, Evolutionary Fuzzy K-Nearest Neighbors classifier using Interval-Valued Fuzzy Sets (EF-kNN-IVFS), incorporating interval-valued fuzzy sets for computing the memberships of training instances in Fuzzy-kNN. It is based on the representation of multiple choices of two key parameters of Fuzzy-kNN: One is applied in the definition o...

• Type: Journal
• Year: 2016
• Where: ISCI
• Authors: Joaquín Derrac, Francisco Chiclana, Salvador García, Francisco Herrera
• Keywords: Artificial Intelligence, ISCI 2016