An Empirical Risk Functional to Improve Learning in a Neuro-Fuzzy Classifier
Giovanna Castellano, Anna Maria Fanelli, Corrado Mencar · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2004
The paper proposes a new Empirical Risk Functional as cost function for training neuro-fuzzy classifiers. This cost function, called Approximate Differentiable Empirical Risk Functional (ADERF), provides a differentiable approximation of the misclassification rate so that the Empirical Risk Minimization Principle formulated in Vapnik's Statistical Learning Theory can be applied. Also, based on the proposed ADERF, a learning algorithm is formulated. Experimental results on a number of benchmark classification tasks are provided and comparison to alternative approaches given.