所属单位：

信息与控制工程学院

发表刊物：

BIFE2009

关键字：

中文关键字：中文文本分类；属性bagging；向量空间模型；信息增益，英文关键字：Chinese text categorization;attribute bagging; vec

摘要：

In order to improve precise rate and recall rate of Chinese text classifier, a improve bagging algorithm – attribute bagging is used in this paper. Document is represented by vector space model and Information Gain is used to select term features. Re-sampling attributes are used to get multiple training sets and the kNN is selected as individual classifier. The classifier 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.

备注：

叶娜

合写作者：

周明全

第一作者：

张翔,叶娜,董丽丽

论文类型：

期刊论文

卷号：

卷：

期号：

期：

页面范围：

页：201-204

是否译文：

否

发表时间：

2009-01-01