Rule extraction from neural networks using fuzzy sets

Abstract

We present an algorithm for extracting if-then rules from a neural network using fuzzy sets. A set of crisp rules each of which is associated with a certainty factor is initially extracted from a trained neural network. The extraction process is based on fuzzy sets. The crisp rules often induce ambiguity areas in the decision space. The certainty factors of the ambiguity areas are transformed into fuzzy sets. A set of rules with confidence values in natural language terms are then extracted. Experiments using the Iris and the Wisconsin breast cancer databases are used to demonstrate the performance of the method.