Propositionalization-based relational subgroup discovery with RSD (2006)
| Venue: | Machine Learning |
| Citations: | 16 - 3 self |
BibTeX
@INPROCEEDINGS{Lavrač06propositionalization-basedrelational,
author = {Filip ˇzelezn´y Nada Lavrač},
title = {Propositionalization-based relational subgroup discovery with RSD},
booktitle = {Machine Learning},
year = {2006},
pages = {33--63}
}
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Abstract
Abstract Relational rule learning algorithms are typically designed to construct classification and prediction rules. However, relational rule learning can be adapted also to subgroup discovery. This paper proposes a propositionalization approach to relational subgroup discovery, achieved through appropriately adapting rule learning and first-order feature construction. The proposed approach was successfully applied to standard ILP problems (East-West trains, King-Rook-King chess endgame and mutagenicity prediction) and two real-life problems (analysis of telephone calls and traffic accident analysis).
