条件先验概率优势关系粗糙集模型

Rough set model based on conditions prior probability dominance relation

基于先验概率优势关系的粗糙集模型是对粗糙集理论的重要扩充,然而却有其不足之处。本研究提出的基于条件先验概率优势关系的粗糙集模型是建立在对不完备偏序关系决策系统属性值数据统计的基础上,既考虑到同一属性取值的不同情况又考虑到不同属性之间的关联性,充分利用各种先验信息,因此有效提高了分类精度和分类质量。理论分析和实例计算均证明了该模型的有效性和实用性。

Rough set model based on prior probability dominance relation is an important expansion of rough set theory. However, it has its own defects and shortcomings. Rough set model based on conditions prior probability domi- nance relation is established on the basis of attribute value data statistics of incomplete partial order relation deci- sion system. It not only takes into account different conditions of the same attribute values, but also the correlation between different attributes, so that a variety of prior information can be fully utilized. Therefore, the classification accuracy and quality can be improved effectively. This new model is proved to be effective and practical by theo- retical analysis and practical example.