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    政大機構典藏 > 理學院 > 資訊科學系 > 會議論文 >  Item 140.119/74458
    Please use this identifier to cite or link to this item: http://nccur.lib.nccu.edu.tw/handle/140.119/74458

    Title: A rule-based classification algorithm: A rough set approach
    Authors: Liao, C.-C.;Hsu, K.-W.
    Contributors: 資科系
    Keywords: 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
    Date: 2012
    Issue Date: 2015-04-10 15:35:10 (UTC+8)
    Abstract: 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.
    Relation: Proceeding - 2012 IEEE International Conference on Computational Intelligence and Cybernetics, CyberneticsCom 2012,1-5
    Data Type: conference
    DOI 連結: http://dx.doi.org/10.1109/CyberneticsCom.2012.6381605
    DOI: 10.1109/CyberneticsCom.2012.6381605
    Appears in Collections:[資訊科學系] 會議論文

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