Experiments with MRDTL -- A Multi-relational Decision Tree Learning Algorithm

Leiva, Hector, Atramentov, Anna and Honavar, Vasant (2002) Experiments with MRDTL -- A Multi-relational Decision Tree Learning Algorithm. Technical Report ISU-CS-TR 02-12, Computer Science, Iowa State University.

Abstract

Experiments with MRDTL -- A Multi-relational Decision Tree Learning Algorithm

Hector Leiva, Anna Atramentov and Vasant Honavar Artificial Intelligence Laboratory Department of Computer Science and Graduate Program in Bioinformatics and Computational Biology Iowa State University Ames, IA 50011, USA www.cs.iastate.edu/~honavar/aigroup.html

Abstract. We describe experiments with an implementation of Multi-relational decision tree learning (MRDTL) algorithm for induction of decision trees from relational databases using an approach proposed by Knobbe et al. [1999a]. Our results show that the performance of MRDTL is competitive with that of other algorithms for learning classifiers from multiple relations including Progol [Muggleton, 1995], FOIL [Quinlan, 1993], Tilde [Blockeel, 1998]. Preliminary results indicate that MRDTL, when augmented with principled methods for handling missing attribute values, could be competitive with the state-of-the-art algorithms for learning classifiers from multiple relations on real-world data sets such as those used in the KDD Cup 2001 data mining competition [Cheng et al., 2002].