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dc.contributor.authorChen, APen_US
dc.contributor.authorChen, MYen_US
dc.date.accessioned2014-12-08T15:37:08Z-
dc.date.available2014-12-08T15:37:08Z-
dc.date.issued2005en_US
dc.identifier.isbn3-540-28895-3en_US
dc.identifier.issn0302-9743en_US
dc.identifier.urihttp://hdl.handle.net/11536/25502-
dc.description.abstractMachine learning methods such as fuzzy logic, neural networks and decision tree induction have been applied to learn rules, however they can get trapped into a local optimal. Based on the principle of natural evolution and global searching, a genetic algorithm is promising for obtaining better results. This article adopts the learning classifier systems (LCS) technique to provide a hybrid knowledge integration strategy, which makes for continuous and instant teaming while integrating multiple rule sets into a centralized knowledge base. This paper makes three important contributions: (1) it provides a knowledge encoding methodology to represent various rule sets that are derived from different sources, and that are encoded as a fixed-length bit string; (2) it proposes a knowledge integration methodology to apply genetic operations and credit assignment to generate optimal rule sets; (3) it uses three criteria (accuracy, coverage, and fitness) to apply the knowledge extraction process, which is very effective in selecting an optimal set of rules from a large population. The experiments prove that the rule sets derived by the proposed approach is more accurate than the Fuzzy ID3 algorithm.en_US
dc.language.isoen_USen_US
dc.titleAn implementation of learning classifier systems for rule-based machine learningen_US
dc.typeArticle; Proceedings Paperen_US
dc.identifier.journalKNOWLEDGE-BASED INTELLIGENT INFORMATION AND ENGINEERING SYSTEMS, PT 2, PROCEEDINGSen_US
dc.citation.volume3682en_US
dc.citation.spage45en_US
dc.citation.epage54en_US
dc.contributor.department資訊管理與財務金融系 註:原資管所+財金所zh_TW
dc.contributor.departmentDepartment of Information Management and Financeen_US
dc.identifier.wosnumberWOS:000232722200007-
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