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An improved TF-IDF approach for text classification
被引：11
作者：
张云涛
龚玲
王永成
机构：
[1] China
[2] Shanghai 200030
[3] Network & Information Center School of Electronic & Information Technology
[4] Shanghai Jiaotong University
基金：
中国国家自然科学基金;
关键词：
Term frequency/inverse document frequency (TF-IDF);
Text classification;
Confidence;
Support;
Characteristic words;
D O I：
暂无
中图分类号：
TP391.1 [文字信息处理];
学科分类号：
081203 ;
0835 ;
摘要：
This paper presents a new improved term frequency/inverse document frequency (TF-IDF) approach which uses confidence, support and characteristic words to enhance the recall and precision of text classification. Synonyms defined by a lexicon are processed in the improved TF-IDF approach. We detailedly discuss and analyze the relationship among confidence, recall and precision. The experiments based on science and technology gave promising results that the new TF-IDF approach improves the precision and recall of text classification compared with the conventional TF-IDF approach.
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页码：50 / 56
页数：7
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