An enhancement of K-Nearest Neighbor algorithm using information gain and extension relativity

被引：0
|
作者：
Wang Baobao [1 ]
Mao Jinsheng [2 ]
Shao Minru [1 ]
机构：
[1] Xidian Univ, Dept Comp Sci, Xian 710071, Peoples R China
[2] ShanXi YunCheng Power Co, YunCheng 044000, Peoples R China
来源：
PROCEEDINGS OF 2008 INTERNATIONAL CONFERENCE ON CONDITION MONITORING AND DIAGNOSIS | 2007年
关键词：
K-Nearest Neighbor algorithm; information; gain; information entropy; extension relativity CLC number-TP182;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
An enhanced K-NN algorithm is proposed in this paper to improve the conventional K-NN algorithm. The enhanced K-NN algorithm proposed uses information gain and extension relativity. The weight coefficient is got through computing the information gain of attributes. In this approach, the anti-jamming ability and accuracy of the K-NN algorithm is improved highly, and the computing time is reduced and the time is improved greatly. The test results show that the novel K-NN algorithm is feasible and effective.
引用
收藏
页码：1314 / +
页数：2
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