Many improved versions of k-nearest neighbor (KNN) have been proposed by minimizing total distances
of multi k nearest neighbors (multi-voter) in each class instead of the majority voting, such as a local
mean-based pseudo nearest neighbor (LMPNN) that give a better decision. In this paper, a new KNN variant called multi-voter multi-commission nearest neighbor (MVMCNN) is proposed to examine its benefits in enhancing the LMPNN. As the name suggests, MVMCNN uses some commissions: each calculates
the total distance between the given query point (test pattern) and k pseudo nearest neighbors using the
LMPNN scheme. The decision class is defined by minimizing those total distances. Hence, the decision in
MVMCNN is obtained more locally than LMPNN. Examination based on 10-fold cross-validation shows
that the proposed multi-commission scheme can enhance the original (single-commission) LMPNN.
Compared with two single-voter models: KNN and Bonferroni Mean Fuzzy k-Nearest Neighbors (BMFKNN), the proposed MVMCNN also gives lower mean error rates as well as higher Precision, Recall,
and F1 Score, indicating that the multi-voter model provides a better decision than the single-voter ones