Concepts for Probabilistic and Possibilistic Induction of Decision Trees on Real World Data

The induction of decision trees from data is a well-known method for learning classifiers. The success of this method depends to a high degree on the measure used to select the next attribute, which, if tested, will improve the accuracy of the classification. This paper examines some possibility-based selection measures and compares them to probabilityand information-based measures on real world datasets. The results show that possibility-based measures do not much worse with regard to classification accuracy, in certain cases they seem to do even slightly better.