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• W2045358009 startingPage "3356" @default.
• W2045358009 abstract "Rough set theory can be applied to rule induction. There are two different types of classification rules, positive and boundary rules, leading to different decisions and consequences. They can be distinguished not only from the syntax measures such as confidence, coverage and generality, but also the semantic measures such as decision-monotocity, cost and risk. The classification rules can be evaluated locally for each individual rule, or globally for a set of rules. Both the two types of classification rules can be generated from, and interpreted by, a decision-theoretic model, which is a probabilistic extension of the Pawlak rough set model. As an important concept of rough set theory, an attribute reduct is a subset of attributes that are jointly sufficient and individually necessary for preserving a particular property of the given information table. This paper addresses attribute reduction in decision-theoretic rough set models regarding different classification properties, such as: decision-monotocity, confidence, coverage, generality and cost. It is important to note that many of these properties can be truthfully reflected by a single measure γ in the Pawlak rough set model. On the other hand, they need to be considered separately in probabilistic models. A straightforward extension of the γ measure is unable to evaluate these properties. This study provides a new insight into the problem of attribute reduction." @default.
• W2045358009 created "2016-06-24" @default.
• W2045358009 creator A5003085848 @default.
• W2045358009 creator A5063696855 @default.
• W2045358009 date "2008-09-01" @default.
• W2045358009 modified "2023-10-11" @default.
• W2045358009 title "Attribute reduction in decision-theoretic rough set models" @default.
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• W2045358009 doi "https://doi.org/10.1016/j.ins.2008.05.010" @default.
• W2045358009 hasPublicationYear "2008" @default.
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