Abstract

In this paper, a novel algorithm for enhancing the performance of classification is proposed. This new method provides rich information for clustering and outlier detection. We call it Natural Nearest Neighbor with Quality (3N-Q). Comparing to K-nearest neighbor and E-nearest neighbor, 3N-Q employs a completely different concept to find the nearest neighbors passively, which can adaptively and automatically get the K value. This value as well as distribution of neighbors and frequency of being neighbors of others offer precious foundation not only in classification but also in clustering and outlier detection. Subsequently, we propose a fitness function that reflects the quality of each training sample, retaining the good ones while eliminating the bad ones according to the quality threshold. From the experiment results we report in this paper, it is observed that 3N-Q is efficient and accurate for solving data mining problems.

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