A new weighting algorithm for linear classifier

被引：0
|
作者：
Chen, KL [1 ]
Zong, CQ [1 ]
机构：
[1] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
来源：
2003 INTERNATIONAL CONFERENCE ON NATURAL LANGUAGE PROCESSING AND KNOWLEDGE ENGINEERING, PROCEEDINGS | 2003年
关键词：
weighting algorithm; variance; text categorization;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the domain of text categorization (TC), the TF (term frequency)*IDF (inverse document frequency) weighting algorithm and TF*IWF*IWF weighting algorithm are widely used. However, the two algorithms are too biased by the term frequency and neglect the unbalance between classes. In this paper., we propose a new weighting algorithm, which is named as TF (term frequency) *IWF (inverse word frequency)*IWF (inverse word frequency)*VE (variance and expectation). The new algorithm improves the TF*IWF*IWF weighting algorithm in both TF and VE. This paper compares the new algorithm with TF*IWF*IWF algorithm respectively in theory and experiment. From the preliminary experiment, we find that the F1-Measure has been improved for 11.78%.
引用
收藏
页码：650 / 655
页数：6
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