Converting Declarative Rules into Decision Trees

Most of the methods that generate decision trees for a specific problem use examples of data instances in the decision tree generation process. This paper proposes a method called “RBDT-1”rule based decision tree -for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. RBDT-1 method uses a set of declarative rules as an input for generating a decision tree. The method’s goal is to create on-demand a short and accurate decision tree from a stable or dynamically changing set of rules. We conduct a comparative study of RBDT-1 with existing decision tree methods based on different problems. The outcome of the study shows that in terms of tree complexity (number of nodes and leaves in the decision tree) RBDT-1 compares favorably to AQDT-1, AQDT-2 which are methods that create decision trees from rules. RBDT-1 compares favorably also to ID3 while is as effective as C4.5 where both (ID3 and C4.5) are famous methods that generate decision trees from data examples. Experiments show that the classification accuracies of the different decision trees produced by the different methods under comparison are equal. Key Words— attribute selection criteria, data-based decision tree , decision rules, rule-based decision tree, tree complexity.