Classification Using the Local Probabilistic Centers of k-Nearest Neighbors
Bo Yu Li, Yun Wen Chen · 2006
In high dimensional feature space with finite samples, severe bias can be introduced in the nearest neighbor algorithm. In this paper, we propose a new classification method, which performs classification task based on local probability center of each class. Moreover, this prototypebased method classifies the query sample by using two measures, one is the distance between query and local probability centers, the other is the posterior probability of query. Although both measures are effect, the experiments show the second one is the better. The investigation results prove that this method improves the classification performance of nearest neighbor algorithm substantially.