A DECISION-THEORETIC ROUGH SET APPROACH TO LATTICE-VALUED INFORMATION SYSTEM

被引：0

作者：

Yu, Jian-Hang ^{[1
,2
]}

Morita, Hiroshi ^{[2
]}

Chen, Ming-Hao ^{[1
]}

Xu, Wei-Hua ^{[3
]}

机构：

[1] Harbin Inst Technol, Dept Math, Harbin 150001, Peoples R China

[2] Osaka Univ, Grad Sch Informat Sci & Technol, Suita, Osaka 5650871, Japan

[3] Southwest Univ, Sch Math & Stat, Chongqing 400715, Peoples R China

来源：

PROCEEDINGS OF 2019 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC) | 2019年

基金：

中国国家自然科学基金;

关键词：

Attribute reduction; Decision-theoretic rough set; Lattice valued information system; Minimum decision cost; FUZZY;

D O I：

10.1109/icmlc48188.2019.8949263

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The decision-theoretic rough set utilizes Bayesian decision to interpret the thresholds of probabilistic rough set model. That provides a novel semantic description for rough regions in the viewpoint of three-way decision theory and has been applied to numerous fields. However, it lacks the ability to deal with lattice valued information system (LvIS), in which the condition attribute set consists of multiple types of attributes and their domain constitute lattice. Therefore, this study concentrates on the decision theoretic rough approach in a LvIS. Then, the total decision cost associated with rough regions is addressed and an attribute reduction algorithm will be designed based on minimum decision cost. Finally, a case study on medical diagnosis is conducted to illustrate the decision procedure and attribute reduction approach.

引用

页码：78 / 83

页数：6

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