KNN Algorithm Improving Based on Cloud Model

摘要：

KNN algorithm is particularly sensitive to outliers and noise contained in the training data set In this paper, we use the reverse cloud algorithm to map the training samples into clouds. Each attribute is mapped to a cloud vector. Reverse cloud algorithm is not sensitive to the noise on data sets and it can eliminate the impact of noise on classification effectively. By comparing the similarity of clouds in the cloud vector, we can calculate the attributes weights. For those attributes with a low weight of properties, we find out merger them to a new attribute which can generate more significant attribute weight than original ones. We present a new KNN algorithm based on Cloud Model and compare our algorithm with classic KNN algorithms and other wellknown improved KNN algorithms using 10 data sets. Experiments show that our approach could achieve a better or at least a comparable classification accuracy with other algorithms.

关键词： KNN classification attribute weight learning similarity Cloud Model

作者: Liu Yu Chen Gui-Sheng

作者单位: State Key Lab of Software Development Environment Beihang University Beijing, China China Institute of Electronics Engineering Beijing, China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 63-66

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）