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When classes are nonseparable or overlapping, training samples in a local neighborhood may come from different classes. In this situation, the samples with different class labels may be comparable in the neighborhood of query. As a consequence, the conventional nearest neighbor classifier, such as -nearest neighbor scheme, may produce a wrong prediction. To address this issue, in this paper, we propose a new classification method, which performs a classification task based on the local probabilistic centers of each class. This method works by reducing the number of negative contributing points, which are the known samples falling on the wrong side of the ideal decision boundary, in a training set and by restricting their influence regions. In classification, this method classifies the query sample by using two measures of which one is the distance between the query and the local categorical probability centers, and the other is the computed posterior probability of the query. Although both measures are effective, the experiments show that the second one achieves the smaller classification error. Meanwhile, the theoretical analyses of the suggested methods are investigated, and some experiments are conducted on the basis of both constructed and real datasets. The investigation results show that this method substantially improves the classification performance of the nearest neighbor algorithm.