An Adaptive Rule Based on Unknown Pattern for Improving K-Nearest Neighbor Classifier

Abstract

One of popular and simple pattern classification algorithms is the k-nearest neighbor rule. However, it often fails to work well when patterns of different classes overlap in some regions in the feature space. To overcome this problem, many researches strive for developing various adaptive or discriminatory metrics to improve its performance for classification, recently. In this paper, we proposed a simple adaptive nearest neighbor rule on distance measure for two objects. First one is to separate the overlapping data, and the second one is to avoid the influence of outliers. From the experimental results, our method is robust for the choice of the number of k and outperforms than k-nearest neighbor classifier.