A Text Classification Model Based on Training Sample Selection and Feature Weight Adjustement

摘要：

A new text classification model based on training samples sdection and feature weight adjustment is presented. First it computes representativeness score of samplts so as to dishnguish noise samples from original training samples. Then a feature weight adjustment taking inter-class distribution and intra-class distribuhon into consideration is used to further improve the performance of text classification. The presented text classification model is applied on Chinese text dataset provided by Fudan Database Center. The experiments show that the proposed model can improve the performance of text classification to some extent with fewer training samples and fewer feature dimensions.

关键词： textclarssification representativeness score training dataset selection feature weight adjustment

作者: Xuezeng Pang Yixing Liao

作者单位: Department of Computer Science & Technology Zhejiang University Hangzhou, China Department of Computer Science & Technology Zhejiang University, Hangzhou,China Department of Inform

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: 294-297

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）