A neuro-fuzzy approach for feature selection
José M. Benítez, Juan Luis Castro, Carlos Javier Mantas, Fernando Rojas · 2002
A method for feature selection based on a combination of artificial neural network and fuzzy techniques is presented. The procedure produces a ranking of features according to their relevance to the network. This ranking is used to perform a backward selection by successively removing input nodes in a network trained using the complete set of features as inputs. Irrelevant input units and their connections are pruned. The remaining biases are adjusted in such a way that the overall change in the behavior learnt by the network is under control. When the problem is too hard to be modeled by a single network, several of them are used to generate different rankings which are aggregated by using a fuzzy logic operator. The proposed method is applied on a number of real-world classification problems. Empirical results show that the feature selection enables the network to improve its generalization ability. This procedure also offers several advantages with respect to other feature selection methods, especially improved efficiency.