Knowledge Discovery and Model Structure Selection Based on Rough Set Theory
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摘要: 为了合理选择决策模型,提出了一种基于粗糙集理论获取模型选择知识并利用获取的知识选择模型的方法.通过随机设定模型参数,得到备选模型的属性值为连续值的决策表;通过设定误差范围,获得决策表中对象之间的关系;利用该关系和粗糙集理论,对决策表进行属性约简,获得决策规则;按该决策规则进行推理,实现智能决策的模型选择.实例表明,该算法是可行的.Abstract: To select a proper decision model in intelligent decision,a method to acquire the knowledge of model selection and select a decision model by utilizing the knowledge was proposed based on the rough set theory.With this method,the decision table of models with continuous attribute values is acquired by setting parameters in the models randomly.Then,the relation between objects in the decision table is obtained by setting errors.From the relation,the reduction of attributes for the decision table is conducted by applying the rough set theory to gain decision rules.Based on these decision rules,a proper model can be selected. The feasibility of the proposed method has been illustrated using an example.
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Key words:
- IDSS(intelligent decision support system) /
- rough set /
- model selection /
- decision rule
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