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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/4724


    Title: A genetics-based approach for knowledge integration and refinement
    Authors: C. H. Wang;T. P. Hong;S. S. Tseng
    Contributors: Department of Information Science and Applications
    Date: 2001-01
    Issue Date: 2009-11-30 16:03:17 (UTC+8)
    Publisher: Asia University
    Abstract: 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.
    Relation: Journal of Inforamtion Science and Engineering 17(1):85-94
    Appears in Collections:[行動商務與多媒體應用學系] 期刊論文

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