Time Series Classification Based on Attributes Weighted Sample Reducing KNN

摘要：

KNN is widely used in classification, but it could not gain good performance for multi-attribute time series classifying. According to the characteristic of multiattribute time series and shortage of KNN, the attributes weighted sample reducing KNN classification approach-WRKNN is proposed. Two major aspects are improved for KNN classification, one is to give weight to the attributes of time series; the other one is to reduce the training set to relative equal density based on weighted distance. A equally distributed training data set is obtained by the improved KNN approach, and the number of training samples is decreased at the same time, hence the efficiency and accuracy is enhanced. At last, the feasible of WRKNN is tested by the experiment.

关键词： KNN time series classification weight reducing

作者: Shaoqing Xu Qiangyi Luo Huabo Li Lei Zhang

作者单位: Institute of Command Automation, PLA University of Science and Technology Nanjing, China Institute o Institute of Electronic Equipment System Engineering Corporation Beijing China Institute of Command Automation, PLA University of Science and Technology Nanjing China Institute of Command Automation PLA University of Science and Technology Nanjing China

会议类型: 国际会议

会议名称: Second International Symposium on Electronic Commerce and Security(第二届电子商务与安全国际研究大会)(ISECS 2009)

会议地点: 南昌

会议语种:英文

页码: 850-855

在线出版日期: 2009-05-22（万方平台首次上网日期，不代表论文的发表时间）