A new fuzzy k-nearest neighbor classifier based on the Bonferroni mean - Mailagaha Kumbure, Mahinda; Luukka, Pasi; Collan, Mikael (Pattern recognition letters, 2020)
•Two new variants of the k-nearest neighbor classifier are introduced, both use the Bonferroni mean.•The new variants are less sensitive to class imbalances than their original counterparts.•The proposed methods produce improved classification accuracy compared to benchmarks•The methods achieve the best accuracy with higher values of k neigbors than the used KNN-based benchmarks. We present a new generalized version of the fuzzy k-nearest neighbor (FKNN) classifier that uses local mean vectors and utilizes the Bonferroni mean. We call the proposed new method Bonferroni-mean based fuzzy k-nearest neighbor (BM-FKNN) classifier. 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. The performance of the proposed classifier is tested with six real-world data sets and with one artificial data set. The results are benchmarked with classification results obtained with the classical k-nearest neighbor-, the local mean-based k-nearest neighbor-, the fuzzy k-nearest neighbor- and other three selected classifiers. In addition to this, an enhancement of the local mean-based k-nearest neighbor classifier by using the Bonferroni means is also proposed and tested. The results show that the proposed new BM-FKNN classifier has the potential to outperform the benchmarks in classification accuracy and confirm the usefulness of using the Bonferroni mean in the learning part of classifiers.