A generalized model of covering rough sets and its application in medical diagnosis

Author

Li, Yan ; Feng, Tao ; Zhang, Shao-pu ; Li, Zhan-wen

Author_Institution

Coll. of Sci., Hebei Univ. of Sci. & Technol., Shijiazhuang, China

Volume

1

fYear

2010

fDate

11-14 July 2010

Firstpage

145

Lastpage

150

Abstract

In the covering information system with decision-making (CISD), τ-lower and upper approximation operators are introduced, and some corresponding properties are discussed. This paper also explores reductions of a covering which is based on a new concept of the degree of approximate dependency, and proposes a reduction algorithm based on the importance degree. After reduction, a decision tree is generated and rules are extracted from the decision tree. Finally, the above mentioned method is demonstrated by an example in medical diagnosis.

Keywords

decision making; decision trees; knowledge acquisition; learning (artificial intelligence); medical computing; patient diagnosis; rough set theory; approximation operator; covering information system; decision making; decision tree; medical diagnosis; rough set; rule extraction; Approximation methods; Decision making; Decision trees; Diseases; Information systems; Machine learning; Rough sets; τ — lower and upper approximation; Covering information system; Decision tree; reduction;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2010 International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4244-6526-2

Type

conf

DOI

10.1109/ICMLC.2010.5581076

Filename

5581076