標題: A coverage-based genetic knowledge-integration strategy
作者: Wang, CH
Hong, TP
Chang, MB
Tseng, SS
資訊工程學系
Department of Computer Science
關鍵字: brain tumor diagnosis;genetic algorithm;knowledge encoding;knowledge integration;credit assignment
公開日期: 1-Jul-2000
摘要: In this paper, we propose a coverage-based genetic knowledge-integration approach to effectively integrate multiple rule sets into a centralized knowledge base. The proposed approach consists of two phases: knowledge encoding and knowledge integration. In the knowledge-encoding phase, each rule in the various rule sets that are derived from different sources (such as expert knowledge or existing knowledge bases) is first translated and encoded as a fixed-length bit string. The bit strings combined together thus form an initial knowledge population. In the knowledge-integration phase, a genetic algorithm applies genetic operations and credit assignment at each rule-string to generate an optimal or nearly optimal rule set. Experiments on diagnosing brain tumors were made to compare the accuracy of a rule set generated by the proposed approach with that of the initial rule sets derived from different groups of experts or induced by various machine learning techniques. Results show that the rule set derived by the proposed approach is more accurate than each initial rule set on its own. (C) 2000 Elsevier Science Ltd. All rights reserved.
URI: http://dx.doi.org/10.1016/S0957-4174(00)00016-6
http://hdl.handle.net/11536/30427
ISSN: 0957-4174
DOI: 10.1016/S0957-4174(00)00016-6
期刊: EXPERT SYSTEMS WITH APPLICATIONS
Volume: 19
Issue: 1
起始頁: 9
結束頁: 17
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