Generating production rules from decision trees

TLDR

Many inductive knowledge acquisition algorithms generate classifiers in the form of decision trees. This paper proposes a technique for converting decision trees into compact sets of production rules, a common knowledge representation in expert systems. The method employs the training data to generalize, evaluate the reliability of extracted rules, and iteratively refine the rule set. The resulting rule sets are typically simpler and more accurate than the original trees, and the approach also enables combining multiple trees for the same domain.

Abstract

Many inductive knowledge acquisition algorithms generate classifiers in the form of decision trees. This paper describes a technique for transforming such trees to small sets of production rules, a common formalism for expressing knowledge in expert systems. The method makes use of the training set of cases from which the decision tree was generated, first to generalize and assess the reliability of individual rules extracted from the tree, and subsequently to refine the collection of rules as a whole. The final set of production rules is usually both simpler than the decision tree from which it was obtained, and more accurate when classifying unseen cases. Transformation to production rules also provides a way of combining different decision trees for the same classification domain.