Classification and Regression Tree Construction

Decision trees, either classification or regression trees, are especially attractive type of models for three main reasons. First, they have an intuitive representation, the resulting model is easy to understand and assimilate by humans [BFOS84]. Second, the decision trees are nonparametric models, no intervention being required from the user, and thus they are very suited for exploratory knowledge discovery. Third, scalable algorithms, in the sense that the performance degrades gracefully with the increase of the size of training data, exist for decision tree construction models [GRG98]. Last, accuracy of decision trees is comparable or superior to other models [Mur95, LLS97]. Three subtopics of decision tree construction received our attention: