Weighted Nearest Neighbor Classification via Maximizing Classification Consistency

The nearest neighbor classification is a simple and effective technique for pattern recognition. The performance of this technique is known to be sensitive to the distance function used in classifying a test instance. In this paper, we propose a technique to learn sample weights via maximizing classification consistency. Experimental analysis shows that the distance trained in this way enlarges the classification consistency on several datasets and has a strong ability to tolerate noise. Moreover, the proposed approach has better performance than nearest neighbor classification and several state-of-the-art methods.