Affiliations: Computer School of National University of Defense Technology, Changsha, Hunan, China
Correspondence:
[*]
Corresponding author: Aiping Li, Computer school of National University of Defense Technology, Changsha 410073, Hunan, China.apli1974@gmail.com
Abstract: Knowledge acquisition plays very important role in the diagnostic expert
system. It usually takes a long period to acquire disease knowledge using
the traditional methods. To solve this problem, this paper describes
relations between rough set theory and rule-based description of diseases,
which corresponds to the process of knowledge acquisition of diagnostic
expert system. Then the exclusive rules, inclusive rules and disease images
of 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 diseases diagnostic expert
system. The experimental results not only show that rough set theory gives a
very suitable framework to represent processes of uncertain knowledge
extraction, but also that this method induces diagnostic rules correctly.
This method can act as the assistant tool for development of diagnosis
expert system, and has an extensive application in intelligent information
systems.
Keywords: Rule induction, rough set, knowledge acquisition, diagnostic expert system
