Abstract: A computer-implemented apparatus and method for generating a rule-based expert system from a trained neural network which is expressed as network data stored in a computer-readable medium. The rule-based expert system represents an interconnected network of neurons with associated weights data and threshold data. A network configuration extractor is provided for accessing the network data and for ascertaining the interconnection structure of the trained neural network by examining the network data. A transformation system is utilized to alter the algebraic sign of at least a portion of the weights data to eliminate differences in the algebraic sign among the weights data while selectively adjusting the threshold data to preserve the logical relationships defined by the neural network. A symbolic representation generator applies a sum-of-products search upon each neuron in the network to generate a multivalued logic representation for each neuron.

Abstract: A computer-implemented apparatus and method for designing decision tree classifiers for use in artificial applications. A tree structure of fixed size with empty internal nodes, i.e. nodes without any splitting function, and labeled terminal nodes is first constructed. Using a collection of training vectors of known classification, a neural learning scheme combining backpropagation and soft competitive learning is then used to simultaneously determine the splits for each decision tree node. Compact trees are generated that have multifeature splits at each internal node which are determined on global rather than local basis. The computer-implemented apparatus and method consequently produces decision trees yielding better classification and interpretation of the underlying relationships in the data.