Feature selection method on imbalanced text

被引：1

作者：

Liao, Yi-Xing ^{[1
,2
]}

Pan, Xue-Zeng ^{[1
]}

机构：

[1] College of Computer Science and Technology, Zhejiang University

[2] Department of Information, Zhejiang University of Finance and Economics

来源：

Dianzi Keji Daxue Xuebao/Journal of the University of Electronic Science and Technology of China | 2012年 / 41卷 / 04期

关键词：

Feature selection; Imbalanced dataset; Strong class-related; Text classification;

D O I：

10.3969/j.issn.1001-0548.2012.04.022

中图分类号：

学科分类号：

摘要：

After analyzing the four basic information elements of traditional feature selection methods, a new measurement of strong class information is introduced and a new feature selection method is proposed for imbalanced text classification. The strong class information and the frequency of terms are used to improve the classification performance of minority classes and majority classes respectively. The experiments on reuter-21578 dataset show that the proposed method is better than IG and CHI. Both Micro F 1 and Macro F 1 are improved to some degree.

引用

页码：592 / 595

页数：3

共 12 条

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