DualPOS: A Semi-supervised Attribute Selection Approach for Symbolic Data Based on Rough Set Theory

被引：11

作者：

Dai, Jianhua ^{[1
,2
]}

Han, Huifeng ^{[2
]}

Hu, Hu ^{[2
]}

Hu, Qinghua ^{[1
]}

Zhang, Jinghong ^{[2
]}

Wang, Wentao ^{[2
]}

机构：

[1] Tianjin Univ, Sch Comp Sci & Technol, Tianjin 300350, Peoples R China

[2] Zhejiang Univ, Coll Comp Sci & Technol, Hangzhou 310027, Zhejiang, Peoples R China

来源：

WEB-AGE INFORMATION MANAGEMENT, PT II | 2016年 / 9659卷

关键词：

Semi-supervised; Attribute selection; Rough set theory; Dual-dependence degree; DualPOS; CONDITIONAL ENTROPY;

D O I：

10.1007/978-3-319-39958-4_31

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Rough set theory, supplying an effective model for representation of uncertain knowledge, has been widely used in knowledge engineering and data mining. Especially, rough set theory has been used as an attribute selection method with much success. However, current rough set approaches for attribute reduction are unsuitable for semi-supervised learning as no enough labeled data can guarantee to calculate the dependency degree. We propose a new attribute selection strategy based on rough sets, called DualPOS. It provides mutual function mechanism of multi-attributes, and generates the most consistent one as a candidate. Experiments are carried out to test the performances of classification and clustering of the proposed algorithm. The results show

引用

页码：392 / 402

页数：11

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