Research Of Attribute Reduction Method Based On Rough Sets And Neural Networks

Rough Set (RS) theory was put forward by Z. Pawlak is law in 1982. After about 20 years of developing, it has received fruitful achievements in both of theory and applications. RS doesn't depend on additional information beyond the data set, which is a potent tool for dealing with vague, imprecise, incomplete and uncertain data, and is a new technology in Data Mining. After the research in the reduction algorithm, a new reduction algorithm based on Attribute importance and Attribute dependencies is proposed. This algorithm proves its validity in theory and in experiment.Artificial neural network (ANN) is a kind of simulation of the biological neural network of human brain. It is constituted by several neurons, which are connected according to some rules and have processing capacities. ANN is suitable for dealing with the fuzzy, problem that inaccuracy data and complicated nonlinearity shine upon.Sensitivity analysis is a common easy method to simplify network structure. The paper have studied RBFNN research in deleting redundant attribute emphatically on the basis of analyzing all kinds of neural networks in thesis. Provide random variable law of asking of derivative of function according to probability definition disappeared to estimate, provide the base function nerve network of the radial during the process of approaching the function, calculation type to each sensitivity of inputting the variable. Through define, make comparisons with sensitivity put forward in the [12] literature, prove the definition of sensitivity way popularization of definition put forward in the [12] literature.