期刊名称：International Journal of Security and Its Applications
印刷版ISSN：1738-9976
出版年度：2015
卷号：9
期号：2
页码：149-158
DOI：10.14257/ijsia.2015.9.2.14
出版社：SERSC
摘要：To overcome the drawback of KNN algorithms based on distance measure which did not measure the contributions for each feature accurately. In this paper, a K-Nearest Neighbor (KNN) de-noise method based on likelihood distance entropy is proposed. The relations of feature parameters are used to measure their contributions for de-noise energy, then according to the contributions for each feature leading de-noise of the feature parameters. In order to compare the performance of these relative methods, the Letter corpora and Pima Indians Diabetes data- base are employ to carry out the experiments, the experiment results show that comparing with the other de-noise methods mentioned in this paper, this proposed method have a better ability for de-noise.
