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Feature ranking using supervised neural gas and informational energy

In this paper we use the maximization of Onicescu's informational energy as a criteria for computing the relevances of input features. This adaptive relevance determination is used in combination with the neural gas and the generalized relevance LVQ algorithms. The idea of applying the neural gas neighborhood cooperation technique to improve the generalized relevance LVQ is due to Hammer et al. and is best described in Hammer et al., 2005. Our approach gives an alternative way for determining the relevances in Hammers's algorithm, and in our experiments it shows at least the same performances. Our contribution is an incremental learning algorithm for supervised classification and feature ranking.