Comparative Evaluation of Approaches to Propositionalization

Propositionalization has already been shown to be a particularly 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 databaseoriented 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, databaseoriented methods can be more efficient especially on larger data sets. Accuracies gained vary such that a combination of the features produced by both groups seems a further valuable venture.

• Type: Conference
• Year: 2003
• Where: ILP
• Authors: Mark-A. Krogel, Simon Rawles, Filip Zelezný, Peter A. Flach, Nada Lavrac, Stefan Wrobel
• Keywords: Data Sets, ILP 2003, Larger Data Sets, Relational Data Sets