Most of the methods that generate decision trees for a specific problem use examples of data instances in the decision tree generation process. We propose a new method called “RBDT-1” (rule based decision tree) for learning a decision tree from a set of decision rules that cover the data instances. 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. The rules used by RBDT-1 could be generated either by an expert or induced directly from a rule induction method or indirectly by extracting them from a decision tree. We conducted a comparative study of RBDT-1 with four existing decision tree methods based on different problems.The outcome of the study showed that in terms of tree complexity (number of nodes and leaves in the decision tree)and accuracy RBDT-1 compares favorably to AQDT-1 and 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.
Learn more on the RBDT-1 project’s website.