| Summary: | 碩士 === 國立交通大學 === 資訊科學學系 === 83 === Knowledge-integration is a very important technique in
developing the expert system, but it sometimes takes much time.
Especially, when the multiple rule-sets are constructed by
multiple experts or induced by various learning algorithms, the
integrating process is tedious. In this paper, we will propose
an automated knowledge-integration approach to integrate
multiple rule-sets. Our approach consists of two phases: rule-
sets encoding and rule-sets integrating. For the encoding
phase, each rule-set is encoded to a bit-string as a member of
an initial population. For the integrating phase, an adaptive
searching technique (genetic algorithm) is used to induce the
optimal concept description from the multiple rule-sets. In the
mean time, experiments in diagnosing brain tumor (DBT) are
schemed to compare the accuracy of knowledge integration with
that of the original rule sets. Experimental results show that
the accuracy concept description can be obtained from the above
mentioned approach and the time consumption of the integrating
process is obviously reduced.
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