Design of Chinese Tezt Categorization Classifier Based on Attribute Bagging

摘要：

In order to improve the precise rate and recall rate of Chinese text classifier, an improved bagging algorithm-attribute bagging is used in this paper. Document is represented by vector space model and Information Gain is used to do the feature selection. Re-sampling attributes is used to get multiple training sets and the kNN is selected as the individual classifier. The classification result is attained by voting. Experiments show that the attribute bagging gets lower errors and better performance than bagging and kNN in Chinese text categorization.

关键词： Chinese tezt categorizatum attribute bagging vector space model information gain

作者: Xiang Zhang Mingquan Zhou Lili Dong Na Ye

作者单位: College of Information Science and Technology Northwest University Xian, China College of Informati College of Information Science and Technology Beijing Normal University Beijing, China College of Information and Control Engineering Xian University of Architecture and Technology Xian

会议类型: 国际会议

会议名称: The Second International Conference on Business Intelligence and Financial Engineering(BIFE 2009)(第二届商务智能与金融工程国际会议)

会议地点: 北京

会议语种:英文

页码: 201-204

在线出版日期: 2009-07-24（万方平台首次上网日期，不代表论文的发表时间）