MIBARK algorithm defined the attribute importance using the information entropy.
MIBARK算法基于信息论的方法，用信息熵来定义属性的重要性。
Proposed an algorithm of attribute reduction based on attribute importance of rough set.
提出一种基于粗糙集属性重要性的属性约简算法。
This paper presents a method of neural network attributes selection based on data attribute importance ranking.
文中提出一种基于数据属性重要性排序的神经网络属性选择方法。
This method discusses how to ascertain the impersonal weights allocation by applying the attribute importance based on the relative positive region conception.
根据粗糙集中的相对正域概念，探讨了如何运用属性重要度确定各属性的客观权重。
This method discusses how to ascertain the impersonal weights allocation by applying the attribute importance based on the relative positive region conception.
根据粗糙集中的相对正域概念，探讨了如何运用属性重要度确定各属性的客观权重。
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