Research of Knowledge Reduction Based on New Conditional Entropy

摘要

Although knowledge reduction for a decision table based on discernibility function can be used widely in data classification, there are also many disadvantages needed discussing detailedly on knowledge acquisition. To make some improvement for them, firstly, the concept of a decision table simplified was put forward for removing redundant data. Then based on knowledge granulation and conditional information entropy, the definition of a new conditional entropy, which could reflect the change of decision ability objectively and equivalently and present the concepts and operations in an inconsistent decision table simplified, was given by separating the consistent objects from the inconsistent objects. Furthermore, many propositions and properties for reduction with an inequality were proposed, and a complete knowledge reduction method was implemented. Finally, the experimental results with UCI data sets show that the proposed method of knowledge reduction is an effective technique to deal with complex data sets, and can simplify the structure and improve the efficiency of data classification.