Automatic construction of decision trees from data: A multi-disciplinary survey

Decision trees have proved to be valuable tools for the description, classi fication and generalization of data. Work on constructing decision trees fr om data exists in multiple disciplines such as statistics, pattern recognit ion, decision theory, signal processing, machine learning and artificial ne ural networks. Researchers in these disciplines, sometimes working on quite different problems, identified similar issues and heuristics for decision tree construction. This paper surveys existing work on decision tree constr uction, attempting to identify the important issues involved, directions th e work has taken and the current state of the art.