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題名 Developing a GP-based Framework for Knowledge Integration
作者 陳春龍
Kuo, Chan-Sheng;Hong, Tzung-Pei;Chen, Chuen-Lung
貢獻者 資管系
關鍵詞 Genetic Programming; Knowledge Integration; Knowledge Base; Genetic Operator
日期 2012
上傳時間 12-Feb-2015 14:44:13 (UTC+8)
摘要 Knowledge integration is one of the important tasks for applying knowledge management in an organization to improve organizational performance and competitive competence. In this paper, we have proposed a GP-based knowledge-integration framework that automatically combines multiple rule sets into one integrated knowledge base. The proposed framework consists of three phases: knowledge collection and translation, knowledge integration, and knowledge output. In the collection and translation phase, each knowledge source is obtained and expressed as a rule set and then translated as a classification tree. In the integration phase, the genetic programming technique is used to generate a nearly optimal classification tree. In the output phase, the final derived classification tree is transferred as a rule set, then output to the knowledge base to facilitate the inference of new data. Two new genetic operators, abridgement and compromise, are designed in the proposed approach to remove redundancy, subsumption and contradiction, thus producing more accurate and concise classification rules than that without using them. Experimental results from diagnosis of breast cancer also show the feasibility of the proposed algorithm.
關聯 Journal of Convergence Information Technology,14(7),79-88
資料類型 article
DOI http://dx.doi.org/10.4156/jcit.vol7.issue14.10
dc.contributor 資管系
dc.creator (作者) 陳春龍zh_TW
dc.creator (作者) Kuo, Chan-Sheng;Hong, Tzung-Pei;Chen, Chuen-Lung
dc.date (日期) 2012
dc.date.accessioned 12-Feb-2015 14:44:13 (UTC+8)-
dc.date.available 12-Feb-2015 14:44:13 (UTC+8)-
dc.date.issued (上傳時間) 12-Feb-2015 14:44:13 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/73502-
dc.description.abstract (摘要) Knowledge integration is one of the important tasks for applying knowledge management in an organization to improve organizational performance and competitive competence. In this paper, we have proposed a GP-based knowledge-integration framework that automatically combines multiple rule sets into one integrated knowledge base. The proposed framework consists of three phases: knowledge collection and translation, knowledge integration, and knowledge output. In the collection and translation phase, each knowledge source is obtained and expressed as a rule set and then translated as a classification tree. In the integration phase, the genetic programming technique is used to generate a nearly optimal classification tree. In the output phase, the final derived classification tree is transferred as a rule set, then output to the knowledge base to facilitate the inference of new data. Two new genetic operators, abridgement and compromise, are designed in the proposed approach to remove redundancy, subsumption and contradiction, thus producing more accurate and concise classification rules than that without using them. Experimental results from diagnosis of breast cancer also show the feasibility of the proposed algorithm.
dc.format.extent 823698 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Journal of Convergence Information Technology,14(7),79-88
dc.subject (關鍵詞) Genetic Programming; Knowledge Integration; Knowledge Base; Genetic Operator
dc.title (題名) Developing a GP-based Framework for Knowledge Integration
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.4156/jcit.vol7.issue14.10en_US
dc.doi.uri (DOI) http://dx.doi.org/10.4156/jcit.vol7.issue14.10 en_US