Investigating the performance of Cosine value and Jensen-Shannon Divergence in the kNN algorithm

摘要：

　　K Nearest Neighbor (kNN) is a commonly-used text categorization algorithm.Previous studies mainly focused on improvements of the algorithm by modifying feature selection and k value selection.This research investigates the possibility to use Jensen-Shannon Divergence as similarity measure in the kNN classifier,and compares the performance,in terms of classification accuracy.The experiment denotes that the kNN algorithm based on Jensen-Shannon Divergence outperforms that based on Cosine value,while the performance is also largely dependent on number of categories and number of documents in a category.

关键词： kNN Jensen-Shannon Divergence Text Categori-zatio Performance

作者: Xiangdong Li Han Jia Li Huang

作者单位: The School of Information Management, Wuhan University, Wuhan 430072, China Wuhan University Library, Wuhan University, Wuhan 430072, China

会议类型: 国际会议

会议名称: 2012 2nd international Conference on Materials Science and Information Technology(2012第二届材料科学与信息技术国际会议)(MSIT2012)

会议地点: 西安

会议语种:英文

页码: 1455-1459

在线出版日期: 2012-08-24（万方平台首次上网日期，不代表论文的发表时间）