Generalized rough set model and its uncertainty measure
Yongmin Li
Abstract
Yongmin Li
Abstract
Some generalizations are needed to remedy the limitations of standard rough set models. A new generalized rough set model is constructed to deal with situations where different data objects have different importance and different attributes have different characteristics using a weighting function for data objects and the attribute characteristic function. This generalized model is used to extend an entropy based uncertainty measure by taking the weight function into account. The method can facilitate combining of factors such as the decision maker's preferences and prior domain knowledge in the rules evaluation processes. The experimental results on a real data set illustrate the advantages of the proposed method.
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Some generalizations are needed to remedy the limitations of standard rough set models. A new generalized rough set model is constructed to deal with situations where different data objects have different importance and different attributes have different characteristics using a weighting function for data objects and the attribute characteristic function. This generalized model is used to extend an entropy based uncertainty measure by taking the weight function into account. The method can facilitate combining of factors such as the decision maker's preferences and prior domain knowledge in the rules evaluation processes. The experimental results on a real data set illustrate the advantages of the proposed method.
Key concepts: Weighting, Rough set, Measure (data warehouse), Entropy (arrow of time), Data mining, Mathematics, Computer science, Function (biology)