计算机应用与软件Issue(9)：254-256,285,4.DOI:10.3969/j.issn.1000-386x.2015.09.061
基于熵降噪优化相似性距离的KNN算法研究
ON KNN ALGORITHM BASED ON OPTIMISING SIMILARITY DISTANCE WITH ENTROPY NOISE REDUCTION
摘要
Abstract
The main purpose of the research in this paper is to find an approach of similarity distance metric with high precision and high efficiency around the KNN algorithm.In this paper,according to the class features of characteristic parameter entropy transform indicator we proposed a similarity distance metric algorithm for reducing the noise of characteristic parameters class,which uses entropy characteristic transform indicator to design the amount of mutual class difference.For noise reduction optimisation of entropy,entropy correlation difference, class credibility calculation,traditional Euclidean distance and several KNN algorithms with same characteristic parameters,the theoretical analysis,simulation experiments on dataset of Letter and Pima Indians Diabetes,as well as KDD CUP’99 practical application of this similarity distance metric all show that the new algorithm is quite effective in KNN.关键词
K近邻分类/熵特征变换/降噪/相似性距离Key words
K-nearest neighbour (KNN)/Entropy characteristic transform/Noise reduction/Similarity distance分类
信息技术与安全科学引用本文复制引用
刘晋胜..基于熵降噪优化相似性距离的KNN算法研究[J].计算机应用与软件,2015,(9):254-256,285,4.基金项目
广东省教育部产学研结合项目（2011 A 090200088）。 （）
