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.
|Translated title of the contribution||Comparative evaluation of approaches to propositionalization|
|Title of host publication||Unknown|
|Editors||Tamas Horvath, Akihiro Yamamoto|
|Publisher||Springer Berlin Heidelberg|
|Pages||194 - 217|
|Number of pages||23|
|Publication status||Published - Oct 2003|