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題名 A rule-based classification algorithm: A rough set approach
作者 Liao, C.-C.;Hsu, K.-W.
廖家奇;徐國偉
貢獻者 資科系
關鍵詞 Attribute-value pairs; Classification algorithm; Classification performance; Decision rules; Indiscernibility; Interpretability; Matrix methods; Nominal datum; Rough set; Rule generation method; Rule induction; Rule-based classification; separate-and-conquer; Understandability; Algorithms; Artificial intelligence; Classification (of information); Learning systems; Rough set theory; Separation; Data mining
日期 2012
上傳時間 10-Apr-2015 15:35:10 (UTC+8)
摘要 In this paper, we propose a rule-based classification algorithm named ROUSER (ROUgh SEt Rule). Researchers have proposed various classification algorithms and practitioners have applied them to various application domains, while most of the classification algorithms are designed with a focus on classification performance rather than interpretability or understandability of the models built using the algorithms. ROUSER is specifically designed to extract human understandable decision rules from nominal data. What distinguishes ROUSER from most, if not all, other rule-based classification algorithms is that it utilizes a rough set approach to decide an attribute-value pair for the antecedents of a rule. Moreover, the rule generation method of ROUSER is based on the separate-and-conquer strategy, and hence it is more efficient than the indiscernibility matrix method that is widely adopted in the classification algorithms based on the rough set theory. On about half of the data sets considered in experiments, ROUSER can achieve better classification performance than do classification algorithms that are able to generate decision rules or trees. © 2012 IEEE.
關聯 Proceeding - 2012 IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012，1-5
10.1109/CyberneticsCom.2012.6381605
資料類型 conference
DOI http://dx.doi.org/10.1109/CyberneticsCom.2012.6381605
dc.contributor 資科系
dc.creator (作者) Liao, C.-C.;Hsu, K.-W.
dc.creator (作者) 廖家奇;徐國偉zh_TW
dc.date (日期) 2012
dc.date.accessioned 10-Apr-2015 15:35:10 (UTC+8)-
dc.date.available 10-Apr-2015 15:35:10 (UTC+8)-
dc.date.issued (上傳時間) 10-Apr-2015 15:35:10 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/74458-
dc.description.abstract (摘要) In this paper, we propose a rule-based classification algorithm named ROUSER (ROUgh SEt Rule). Researchers have proposed various classification algorithms and practitioners have applied them to various application domains, while most of the classification algorithms are designed with a focus on classification performance rather than interpretability or understandability of the models built using the algorithms. ROUSER is specifically designed to extract human understandable decision rules from nominal data. What distinguishes ROUSER from most, if not all, other rule-based classification algorithms is that it utilizes a rough set approach to decide an attribute-value pair for the antecedents of a rule. Moreover, the rule generation method of ROUSER is based on the separate-and-conquer strategy, and hence it is more efficient than the indiscernibility matrix method that is widely adopted in the classification algorithms based on the rough set theory. On about half of the data sets considered in experiments, ROUSER can achieve better classification performance than do classification algorithms that are able to generate decision rules or trees. © 2012 IEEE.
dc.format.extent 176 bytes-
dc.format.mimetype text/html-
dc.relation (關聯) Proceeding - 2012 IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012，1-5
dc.relation (關聯) 10.1109/CyberneticsCom.2012.6381605
dc.subject (關鍵詞) Attribute-value pairs; Classification algorithm; Classification performance; Decision rules; Indiscernibility; Interpretability; Matrix methods; Nominal datum; Rough set; Rule generation method; Rule induction; Rule-based classification; separate-and-conquer; Understandability; Algorithms; Artificial intelligence; Classification (of information); Learning systems; Rough set theory; Separation; Data mining
dc.title (題名) A rule-based classification algorithm: A rough set approach
dc.type (資料類型) conferenceen
dc.identifier.doi (DOI) 10.1109/CyberneticsCom.2012.6381605en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1109/CyberneticsCom.2012.6381605en_US