A new variant of Fuzzy K-Nearest Neighbor using Interval Type-2 Fuzzy Logic
Patricia Melín, Eduardo Gómez Ramírez, German Prado-Arechiga · 2018
In this paper we present a new variant of the Fuzzy K-Nearest Neighbor algorithm. We propose to use Interval Type-2 Fuzzy Logic to improve the performance of the Fuzzy K-Nearest Neighbor algorithm (Fuzzy KNN algorithm). We have used different measures to calculate the distance between the neighbors and the vector to be classified, such as Euclidean, Hamming, cosine similarity and city block distances. These distances represent the inputs for the Interval Type-2 Fuzzy Inference System. Simulation results show the potential of the proposed approach.