Decision Table Reduction Method Based on New Conditional Entropy for Rough Set Theory

Some disadvantages should be discussed deeply for the current reduction algorithms. To eliminate these limitations of classical algorithms based on positive region and conditional information entropy, a new conditional entropy, which could reflect the change of decision ability objectively, was defined with separating consistent objects form inconsistent objects. To select optimal attribute reduction, the judgment theorem of reduction with an inequality was investigated. Condition attributes were considered to estimate the significance for decision classes, and a complete heuristic algorithm was designed and implemented. Finally, through analyzing the given example, the proposed heuristic information is better and more efficient than the others. Comparing the proposed algorithm with these current algorithms through discrete data sets from UCI Machine Learning Repository, the experimental results prove its validity, which enlarges the applied area of rough set.