Introduction:
BM-FKNN is a new generalized version of the fuzzy k-nearest neighbor (FKNN) classifier that uses local mean vectors and utilizes the Bonferroni mean. The BM-FKNN classifier can be easily fitted for various contexts and applications, because the parametric Bonferroni mean allows for problem-based parameter value fitting. The BM-FKNN classifier can perform well also in situations where clear imbalances in class distributions of data are found.

Matlab functions:
The functions of the BM-FKNN algorithm (BM_FKNN.m), Bonferroni mean computation (Bonferrni_mean) are included. In addition to those files, an example (Example.m) of the use of BM_FKNN classifier is also presented. Bonferroni_mean.m is needed to compute Bonferroni mean vectors of the set of nearest neighbor in each class.

Reference: Kumbure, M.M., Luukka,P.& Collan M.(2020) A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean. Pattern Recognition Letters, 140, 172-178.

Created by Mahinda Mailagaha Kumbure & Pasi Luukka, 10/2020
Based on Keller's definition of the fuzzy k-nearest neighbor algorithm.