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    题名: A genetics-based approach for knowledge integration and refinement
    作者: C. H. Wang;T. P. Hong;S. S. Tseng
    贡献者: Department of Information Science and Applications
    日期: 2001-01
    上传时间: 2009-11-30 16:03:17 (UTC+8)
    出版者: Asia University
    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.
    關聯: Journal of Inforamtion Science and Engineering 17(1):85-94
    显示于类别:[行動商務與多媒體應用學系] 期刊論文


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