Mark-A. Krogel, Simon Rawles, Filip Železný, Peter A. Flach, Nada Lavrač, and Stefan Wrobel. Comparative evaluation of approaches to propositionalization. In Tamás Horváth and Akihiro Yamamoto (山本章博), editors, Proceedings of the 13th International Conference on Inductive Logic Programming (ILP 2003), number 2835 in Lecture Notes in Computer Science, pages 197–214, Szeged, Hungary, October 2003. Springer Verlag.
Propositionalization has already been shown to be a promising approach for robustly and effectively handling relational data sets for knowledge discovery. In this paper, we compare up-to-date methods for propositionalization from two main groups: logic-oriented and database-oriented techniques. Experiments using several learning tasks — both ILP benchmarks and tasks from recent international data mining competitions — show that both groups have their specific advantages. While logic-oriented methods can handle complex background knowledge and provide expressive first-order models, database-oriented methods can be more efficient especially on larger data sets. Obtained accuracies vary such that a combination of the features produced by both groups seems a further valuable venture.