Comparative evaluation of approaches to propositionalization

M-A Krogel, S Rawles, F Železný, PA Flach, N Lavrač, S Wrobel

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

57 Citations (Scopus)

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 contributionComparative evaluation of approaches to propositionalization
Original languageEnglish
Title of host publicationUnknown
EditorsTamas Horvath, Akihiro Yamamoto
PublisherSpringer Berlin Heidelberg
Pages194 - 217
Number of pages23
ISBN (Print)3540201440
Publication statusPublished - Oct 2003

Bibliographical note

Conference Proceedings/Title of Journal: Proceedings of the 13th International Conference on Inductive Logic Programming (ILP'2003)

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