Document Feature Selection Based on the Minimum Term Frequency Threshold
CHEN XiaoYun1,2, LI RongLu1, HU YunFa1
1.Department of Computer and Information Technology, Fudan University, Shanghai 200433 2.School of Mathematics and Computer Science, Fuzhou University, Fuzhou 350002
Abstract：In this paper, a novel method of feature evaluation function based on document frequency with the minimum term frequency threshold (DFn) is presented. To decrease the influence of the unrelated features on the system of text categorization, the attribute of the unrelated features is analyzed and the term frequency of the unrelated feature is commonly low. By applying minimum term frequency to filter the low frequency features, the unrelated features are obviously decreased. The experimental results validate the proposed method greatly reduces the number of the unrelated features and effectively improves the accuracy of the text categorization. The improvement to Mutual Information(MI) is very obvious, the Macroaverage F1 value based on DFn is 40% higher than that of Term Frequency, and 15～30% higher than that of Document Frequency(DF).
陈晓云，李荣陆，胡运发. 基于最小词频阈值的文档特征选择*[J]. 模式识别与人工智能, 2006, 19(4): 531-537.
CHEN XiaoYun, LI RongLu, HU YunFa. Document Feature Selection Based on the Minimum Term Frequency Threshold. , 2006, 19(4): 531-537.
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2006, Vol. 19 
