Complete Gini-Index Text (GIT) Feature-Selection Algorithm for Text Classification

摘要：

The recently introduced Gini-Index Text (GIT) feature-selection algorithm for text classification, through incorporating an improved Gini Index for better feature-selection performance, has some drawbacks. Specifically, the algorithm, under real-world experimental conditions,concentrates feature values to one point and be inadequate for selecting representative features. As such, good representative features cannot be estimated, and neither, moreover, can good performance be achieved in unbalanced text classification.Therefore, we suggest a new complete GIT feature-selection algorithm for text classification. The new algorithm, according to experimental results, could obtain unbiased feature values, and could eliminate many irrelevant and redundant features from feature subsets while retaining many representative features.Furthermore, the new algorithm, compared with the original version, demonstrated a notably improved overall classification performance.

关键词： component Gini-Index feature selection text classification

作者: Heum Park Soonho Kwon Hyuk-Chul Kwon

作者单位: AI Lab. Dept. of Computer Science Pusan National University Busan. Korea

会议类型: 国际会议

会议名称: The 2nd International Conference on Software Engineering and Data Mining(IEEE 第二届国际软件工程和数据挖掘学术大会 SEDM 2010)

会议地点: 成都

会议语种:英文

页码: 301-306

在线出版日期: 2010-06-23（万方平台首次上网日期，不代表论文的发表时间）