一种k-NN分类器k值自动选取方法

k-NN 分类算法已广泛应用于文本挖掘和模式识别等领域, 其近邻数k直接影响着分类精度, k 值过小
时k-NN 会受到噪声的影响, k值过大时同样会降低分类精度, 为此提出一种快速选取k值的方法. 首先给出k 值的
候选集, 然后在候选集上快速地选取k 值. 在100 个公开数据集上的实验结果表明, 所提出的算法能够选取一个有效
的近邻数k 是一种效果好、有潜力的方法.

An automatic selection method of k in k-NN classifier

The k-NN classification algorithm has been broadly applied to text mining, pattern recognition and so on. Its
nearest neighbor k has directly effect on its classification accuracy. If the value of  k is too small, k-NN is sensitive to noise.
On the other hand, if  k is too large, its accuracy is also low. On this account, a fast  k value selection method is proposed.
Firstly, a candidate set of  k is calculated. Then an appropriate k is found quickly in this candidate set. Experiment results on
100 publicly available data sets show that, the proposed method can find an effective nearest neighbor successfully, which is
a method of good effect and potential.