Abstract
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 |
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Original language | English |
Title of host publication | Unknown |
Editors | Tamas Horvath, Akihiro Yamamoto |
Publisher | Springer Berlin Heidelberg |
Pages | 194 - 217 |
Number of pages | 23 |
ISBN (Print) | 3540201440 |
Publication status | Published - Oct 2003 |