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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4721


    Title: A coverage-based genetic knowledge-integration strategy
    Authors: C. H. Wang;T. P. Hong;M. B. Chang;S.S. Tseng
    Contributors: Department of Information Science and Applications
    Date: 2000-07
    Issue Date: 2009-11-30 16:03:16 (UTC+8)
    Publisher: Asia University
    Abstract: 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.
    Relation: Expert Systems with Applications 19(1):9-17
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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