Discovering production rules with higher order neural networks: a case study. II

Abstract

It is demonstrated by example that neural networks can be used successfully for automatic extraction of production rules from empirical data. The case considered is a popular public domain database of 8124 mushrooms. With the use of a term selection algorithm, a number of very accurate mask perceptrons (a kind of high-order network or polynomial classifier) have been developed. Then rounding of synaptic weights was applied, leading in many cases to networks with integer weights which were subsequently converted to production rules. It is also shown that focusing of network attention onto a smaller subset of useful attributes ordered with respect to their decreasing discriminating abilities helps significantly in accurate rule generation.<>