Rough computational methods for information systems
Artificial Intelligence
Rough sets and intelligent data analysis
Information Sciences—Informatics and Computer Science: An International Journal
A Comparative Study of Algebra Viewpoint and Information Viewpoint in Attribute Reduction
Fundamenta Informaticae
Knowledge reduction based on granular computing from decision information systems
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
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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.