Constructive theory refinement in knowledge based neural networks

Knowledge based artificial neural networks offer an approach for connectionist theory refinement. We present an algorithm for refining and extending the domain theory incorporated in a knowledge based neural network using constructive neural network learning algorithms. The initial domain theory comprising of propositional rules is translated into a knowledge based network of threshold logic units (threshold neuron). The domain theory is modified by dynamically adding neurons to the existing network. A constructive neural network learning algorithm is used to add and train these additional neurons using a sequence of labeled examples. We propose a novel hybrid constructive learning algorithm based on the Tiling and Pyramid constructive learning algorithms that allows knowledge based neural network to handle patterns with continuous valued attributes. Results of experiments on two non-trivial tasks (the ribosome binding site prediction and the financial advisor) show that our algorithm compares favorably with other algorithms for connectionist theory refinement both in terms of generalization accuracy and network size.