Research And Implement For Attribute Reduction Based On Rough Set

Rough Set theory, initialized by Professor Pawlak in early 1980's, has been proved to be an excellent mathematical tool dealing with uncertain and vague description of objects, whose basic idea is to derive classification rules of conception by knowledge reduction with the ability of classification unchanged. It may find the hiding and potential rules, that is knowledge, from the data without any preliminary or additional information.Firstly, the classical Pawlak Rough Sets based on the equivalence relation is introduced, which approximates sets of object by upper and lower set approximations. Secondly, the problem of attribute reduction is discussed. It has been proved to be NP-Hard to find all reductions and a minimal reduction. We provide the concept of the information quantity of decision attributes given condition attributes, and prove that its changing tendency is decreasing. Then a new attribute reduction algorithm based on information quantity is developed.