中文摘要：

粗糙集方法提供了一种新的处理不精确、不完全与不相容知识的数学工具．属性约简是粗糙集理论的重要研究内容之一，已有的大多数属性约简算法主要针对离散值属性的约简，面向连续值属性的约简报道较少．为此，在引入新的对象一致性定义后，提出一种新的基于一致性准则的属性约简模型，该模型可针对离散或连续值属性进行有效的约简，是经典粗糙集属性约简模型的有效推广．依据新模型，提出了一种基于一致性准则的属性约简算法，该算法可有效进行连续值属性的约简，且通过错分对象数的控制可有效增强属性约简的有效性．理论分析和实验表明提出的算法是有效可行的．

英文摘要：

Rough set theory is a new mathematical tool to deal with imprecise, incomplete and inconsistent data. Attribute reduction is one of important parts researched in rough set theory. Many existing algorithms mainly aim at the reduction of discrete-valued attributes, very little work has been done for attribute reduction aiming to continuous-valued attributes. Therefore, in this paper, after introducing a new definition on consistency of objects, a novel model based on consistency criterion for attribute reduction is introduced. The newly designed model is very suit- able for the decision table with discrete-valued or continuous-valued attributes, and an extension of the classical rough set model. Based on this model, a novel algorithm for attribute reduction based on consistency criterion is proposed. This algorithm can effectively obtain an attribute reduction for the decision table with continuous-valued attributes, and meanwhile the effectiveness of the attribute subset obtained by the new model can be enhanced by controlling the number of the misclassified or consistent objects. Theoretical analysis and experiments shows that the algo- rithm of this paper is efficient and feasible.