Reasoning from Data Computed by Genetic Algorithms Base on Rough Sets Theory

Author

Yang, Wen-yuan ; Ye, Xiao-ping ; Wei, Ping-ping ; Tang, Yong

Author_Institution

Dept. of Comput. Eng., Zhangzhou Inst. of Technol.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

4184

Lastpage

4190

Abstract

Using rough sets to reason from data hinges on three basic concepts of rough sets theory: approximations, decision rules and dependencies. Main objective of reasoning from data is finding hidden patterns in data. Genetic algorithms provides a general frame to optimize problem solution of complex system without depending on the domain of problem, it is robust to many kinds of problem. In this paper we propose a new approach of combining genetic algorithms and rough sets theory to compute reasoning from data by an example of information table, the combination enable us to auto-compute reasoning from data

Keywords

data analysis; decision making; fuzzy set theory; genetic algorithms; inference mechanisms; approximation concept; data analysis; data hinges; decision rules; dependency concept; genetic algorithms; reasoning auto-computing; rough set theory; Computer science; Computer science education; Cybernetics; Data analysis; Data engineering; Educational technology; Fasteners; Genetic algorithms; Genetic engineering; Machine learning; Probability; Robustness; Rough sets; Sun; Genetic Algorithms; Information Table; Pawlak Model Rough Sets Theory; Reasoning from Data;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258940

Filename

4028806