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    政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 >  Item 140.119/73505
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/73505

    Authors: 陳春龍
    Kuo, Chan-Sheng;Hong, Tzung-Pei;Chen, Chun-Lung
    Contributors: 資管系
    Date: 2008
    Issue Date: 2015-02-12 14:44:48 (UTC+8)
    Abstract: Knowledge acquisition can deal with the task of extracting desirable or useful knowledge from data sets for a practical application. In this paper, we have modified our previous gp-based learning strategy to search for an appropriate classification tree. The proposed approach consists of three phases: knowledge creation, knowledge evolution, and knowledge output. In the creation phase, a set of classification trees are randomly generated to form an initial knowledge population. In the evolution phase, the genetic programming technique is used to generate a good classification tree. In the output phase, the final derived classification tree is transferred as a rule set, then outputted to the knowledge base to facilitate the inference of new data. One new genetic operator, separation, is designed in this proposed approach to remove contradiction, thus producing more accurate classification rules. Experimental results from the diagnosis of breast cancers also show the feasibility of the proposed algorithm.
    Relation: Cybernetics and Systems: An International Journal,39(7),672-685
    Data Type: article
    DOI 連結: http://dx.doi.org/10.1080/01969720802257881
    DOI: 10.1080/01969720802257881
    Appears in Collections:[資訊管理學系] 期刊論文

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