Fuzzy Rough Neural Network and Its Application to Feature Selection

For the sake of measuring fuzzy uncertainty and rough uncertainty of real datasets, the fuzzy rough membership function (FRMF) defined in fuzzy rough set is introduced. A new fuzzy rough neural network (FRNN) is constructed based on neural network implementation of FRMF. FRNN has the merits of quick learning and good classification performance. And then a new neural network feature selection algorithm based on FRNN is designed. The input nodes of FRNN are pruned according to the descent of classification accuracy; thereby the search of optimal feature subset is realized with reference to residual input nodes. The test results on UCI datasets show that the algorithm is quick and effective, and has better selection precision and generalization capability than RBF feature selection.