RBDT-1 Method: Combining Rules and Decision Tree Capabilities

Abstract

Most of the methods that generate decision trees for a specific problem use examples of data instances in the decision tree generation process. This chapter 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 which is a famous method that generates 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.KeywordsDecision TreeLeaf NodeDecision StructureDecision ClassDecision Tree MethodThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.