DocumentCode :
2789296
Title :
On Rule Induction Method Based Rough Sets in Diagnostic Expert System
Author :
Li Ai-ping ; Jia Yan ; Wu Quan-yuan
Author_Institution :
Nat. Univ. of Defense Technol., Hunan
Volume :
1
fYear :
2006
fDate :
9-11 Nov. 2006
Firstpage :
392
Lastpage :
398
Abstract :
It usually takes a long period to acquire plant disease knowledge using the traditional methods during the development of expert system. This paper describes relations between rough set theory and rule-based description of plant diseases, which corresponds to the process of knowledge acquisition of expert system. Then the exclusive rules, inclusive rules and disease images of rape-seed disease are built based on the PDES diagnosis model, and the definition of probability rule is put forward. At last, the paper presents the rule-based automated induction reasoning method, including exhaustive search, post-processing procedure, estimation for statistic test and the bootstrap and resampling methods. We also introduce automated induction of the rule-based description, which is used in our plant diseases diagnostic expert system. The experimental results show that rough set theory gives a very suitable framework to represent processes of uncertain knowledge extraction
Keywords :
diagnostic expert systems; diseases; inference mechanisms; knowledge acquisition; rough set theory; bootstrap; diagnostic expert system; disease images; exhaustive search; post-processing procedure; rape-seed disease; resampling methods; rough set theory; rule induction method; rule-based automated induction reasoning method; Biomedical imaging; Diagnostic expert systems; Diseases; Humans; Hypertension; Knowledge acquisition; Medical diagnostic imaging; Probability; Rough sets; Set theory;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Hybrid Information Technology, 2006. ICHIT '06. International Conference on
Conference_Location :
Cheju Island
Print_ISBN :
0-7695-2674-8
Type :
conf
DOI :
10.1109/ICHIT.2006.253517
Filename :
4021120
Link To Document :
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