Neural network methods for rule induction

Local basis function networks are a useful category of classifiers, with known variations developed in neural networks, machine learning and statistics communities. The localized range of activation of the hidden units have many similarities with rule-based representations. Neurofuzzy systems are a common example of a framework that explicitly integrates these approaches. Following this concept, we study alternatives for the development of hybrid rule-neural systems with the purpose of inducing robust and interpretable classifiers. Local fitting of parameters is done by a gradient descent optimization that modifies the covering produced by a rule induction algorithm. Two tasks are accomplished: how to select a small number of rules and how to improve precision. The use of this architecture is better suited when one wants to achieve a good compromise between classification performance and simplicity.