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dc.contributor.author陳宏偉en_US
dc.contributor.authorChen, Hong-Weien_US
dc.contributor.author陳穎平en_US
dc.contributor.authorChen, Ying-Pingen_US
dc.date.accessioned2015-11-26T01:02:11Z-
dc.date.available2015-11-26T01:02:11Z-
dc.date.issued2015en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT079455579en_US
dc.identifier.urihttp://hdl.handle.net/11536/127241-
dc.description.abstractIn this paper, we introduce fault tolerance to XCS and propose a new XCS framework called XCS with Fault Tolerance (XCS/FT). As an important branch of learning classifier systems, XCS has been proven capable of evolving maximally accurate, maximally general problem solutions. However, in practice, it oftentimes generates a lot of rules, which lower the readability of the evolved classification model, and thus, people may not be able to get the desired knowledge or useful information out of the model. Inspired by the fault tolerance mechanism proposed in field of data mining, we devise a new XCS framework by integrating the concept and mechanism of fault tolerance into XCS in order to reduce the number of classification rules and therefore to improve the readability of the generated prediction model. The workflow and operations of the XCS/FT framework are described in detail. A series of N-multiplexer experiments, including 6-bit, 11-bit, 20-bit, and 37-bit multiplexers, are conducted to examine whether XCS/FT can accomplish its goal of design. According to the experimental results, XCS/FT can offer the same level of prediction accuracy on the test problems as XCS can, while the prediction modelzh_TW
dc.description.abstractIn this paper, we introduce fault tolerance to XCS and propose a new XCS framework called XCS with Fault Tolerance (XCS/FT). As an important branch of learning classifier systems, XCS has been proven capable of evolving maximally accurate, maximally general problem solutions. However, in practice, it oftentimes generates a lot of rules, which lower the readability of the evolved classification model, and thus, people may not be able to get the desired knowledge or useful information out of the model. Inspired by the fault tolerance mechanism proposed in field of data mining, we devise a new XCS framework by integrating the concept and mechanism of fault tolerance into XCS in order to reduce the number of classification rules and therefore to improve the readability of the generated prediction model. The workflow and operations of the XCS/FT framework are described in detail. A series of N-multiplexer experiments, including 6-bit, 11-bit, 20-bit, and 37-bit multiplexers, are conducted to examine whether XCS/FT can accomplish its goal of design. According to the experimental results, XCS/FT can offer the same level of prediction accuracy on the test problems as XCS can, while the prediction modelen_US
dc.language.isoen_USen_US
dc.subject分類器zh_TW
dc.subject容錯機制zh_TW
dc.subject資料探勘zh_TW
dc.subjectXCSen_US
dc.subjectFault Toleranceen_US
dc.subjectData Miningen_US
dc.subjectClassificationen_US
dc.title引入容錯機制進入XCS分類器zh_TW
dc.titleIntroducing Fault Tolerance to XCSen_US
dc.typeThesisen_US
dc.contributor.department資訊科學與工程研究所zh_TW
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