Predicting the number of nearest neighbors for the k-NN classification algorithm
Title:
Predicting the number of nearest neighbors for the k-NN classification algorithm
Author:
Zhang, Xueying Song, Qinbao
Appeared in:
Intelligent data analysis
Paging:
Volume 18 (2014) nr. 3 pages 449-464
Year:
2014-05-14
Contents:
k-Nearest Neighbor (k-NN) is one of the most widely used classification algorithms. When classifying a new instance, k-NN first finds out its k nearest neighbors, and then classifies it by voting for the categories of the k nearest neighbors. Therefore, an appropriate number of nearest neighbors is critical for the k-NN classifier. However, in present, there is no systematical solution to determine the specific value of k. In order to address this problem, we propose a novel method of using back-propagation neural networks to explore the relationship between data set characteristics and the optimal values of k, then the relationship and the data set characteristics of a new data set are used to recommend the value of k for this data set. The experimental results on the 49 UCI benchmark data sets show that compared with the optimal k values, although there is a decrease of 1.61% in the average classification accuracy for the k-NN classifier with the recommended k values, the time for determining the k values is greatly shortened.
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