Abstract
kNN is a simple, but effective and powerful lazy learning algorithm. It has been now widely used in practice and plays an important role in pattern classification. However, how to choose an optimal value of k is still a challenge, which straightforwardly affects the performance of kNN. To alleviate this problem, in this paper we propose a new learning algorithm under the framework of kNN. The primary characteristic of our method is that it adopts mutual nearest neighbors, rather than k nearest neighbors, to determine the class labels of unknown instances. The advantage of mutual neighbors is that pseudo nearest neighbors can be identified and will not be taken into account during the prediction process. As a result, the final result is more reasonable. Experimental results conducted on UCI datasets show that the classification performance achieved by our proposed method is better than the traditional one.