標題: A genetics-based approach to knowledge integration and refinement
作者: Wang, CH
Hong, TP
Tseng, SS
資訊工程學系
Department of Computer Science
關鍵字: brain tumor;expert system;genetic algorithm;knowledge integration;knowledge refinement
公開日期: 1-Jan-2001
摘要: In this paper, we propose a genetics-based knowledge integration approach to integrate multiple rule sets into a central rule set. The proposed approach consists of two phases: knowledge encoding and knowledge integrating. In the encoding phase, each knowledge input is translated and expressed as a rule set, and then encoded as a bit string. The combined bit strings form an initial knowledge population, which is then ready for integrating. In the knowledge integration phase, a genetic algorithm generates an optimal or nearly optimal rule set from these initial knowledge inputs. Furthermore, a rule-refinement scheme is proposed to refine inference rules via interaction with the environment. Experiments on diagnosing brain tumors were carried out to compare the accuracy of a rule set generated by the proposed approach with that of initial rule sets derived from different groups of experts or induced by means of various machine learning techniques. Results show that the rule set derived using the proposed approach is much more accurate than each initial rule set on its own.
URI: http://hdl.handle.net/11536/29969
ISSN: 1016-2364
期刊: JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
Volume: 17
Issue: 1
起始頁: 85
結束頁: 94
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