Moderating k-NN Classifiers

Abstract

: The performance of a multiple classifier system combining the soft outputs of k-Nearest Neighbour (k-NN) Classifiers by the product rule can be degraded by the veto effect. This phenomenon is caused by k-NN classifiers estimating the class a posteriori probabilities using the maximum likelihood method. We show that the problem can be minimised by marginalising the k-NN estimates using the Bayesian prior. A formula for the resulting moderated k-NN estimate is derived. The merits of moderation are examined on real data sets. Tests with different bagging procedures indicate that the proposed moderation method improves the performance of the multiple classifier system significantly.