Hybrid BRAINNE: a method for developing symbolic disjunctive rules from a hybrid neural network

A method for learning disjunctive rules using a combination of two existing neural network schemes is proposed. The hybrid network consists of two layers; the first is an unsupervised network while the second is a supervised network. The first layer is used for ordering the inputs of training instances into clusters. Initial rules are extracted from this layer using an existing technique called Unsupervised BRAINNE. These rules are then fed into the second layer which is trained using the delta rule. The second layer is then examined to determine which clusters define the output nodes. This method is able to identify disjunctive rules directly rather than utilising a generate and test paradigm as was used in previous supervised versions of BRAINNE.