Decision Table Reduction based on Conditional Information Entropy
Decision Table Reduction based on Conditional Information Entropy
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发表时间：
2002
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通讯作者：
Wang Guo
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作者：
Wang Guo
分析了粗糙集理论的信息观，并将其与粗糙集理论的代数观进行了比较。通过比较，得出了粗糙集理论的信息观和代数观之间的等价关系和包含关系。提出了两种基于条件信息熵的启发式知识约简算法，即基于条件熵的带计算核知识约简算法(CEBARKCC)和基于条件熵的无计算核知识约简算法(CEBARKNC)。通过理论分析和实验模拟，将这两种算法与多千苗族提出的基于互信息的知识约简算法(MIBARK)进行了比较。CEBARKCC算法和CEBARKNC算法具有较好的仿真性能。
This paper analyzes the information view of rough set theory and compares it with the algebra view of rough set theory. Some equivalence relations and other kind of relations like inclusion relation between the information view and the algebra view of rough set theory are resulted through comparing each other. Two novel heuristic knowledge reduction algorithms are developed based on conditional information entropy, that is, conditional entropy based algorithm for reduction of knowledge with computing core (CEBARKCC) and conditional entropy based algorithm for reduction of knowledge without computing core (CEBARKNC). These two algorithms are compared with a mutual information based algorithm for reduction of knowledge (MIBARK) of Duoqian Miao through theoretical analysis and experimental simulation. CEBARKCC algorithm and CEBARKNC algorithm have good performance in simulation.