Janssen, Frederik ; Fürnkranz, Johannes (2007)
Meta-Learning Rule Learning Heuristics.
Report, Bibliographie
Kurzbeschreibung (Abstract)
The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance from training set characteristics. We investigate several variations of this basic scenario, including the question whether it is better to predict the performance of the candidate rule itself of the resulting final rule. Our experiments on a number of independent evaluation sets show that the learned heuristics outperform standard rule learning heuristics. We also analyze their behavior in coverage space.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2007 |
| Autor(en): | Janssen, Frederik ; Fürnkranz, Johannes |
| Art des Eintrags: | Bibliographie |
| Titel: | Meta-Learning Rule Learning Heuristics |
| Sprache: | Englisch |
| Publikationsjahr: | 2007 |
| URL / URN: | http://www.ke.informatik.tu-darmstadt.de/publications/report... |
| Kurzbeschreibung (Abstract): | The goal of this paper is to investigate to what extent a rule learning heuristic can be learned from experience. Our basic approach is to learn a large number of rules and record their performance on the test set. Subsequently, we train regression algorithms on predicting the test set performance from training set characteristics. We investigate several variations of this basic scenario, including the question whether it is better to predict the performance of the candidate rule itself of the resulting final rule. Our experiments on a number of independent evaluation sets show that the learned heuristics outperform standard rule learning heuristics. We also analyze their behavior in coverage space. |
| ID-Nummer: | TUD-KE-2007-02 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Knowledge Engineering |
| Hinterlegungsdatum: | 24 Jun 2011 15:26 |
| Letzte Änderung: | 26 Aug 2018 21:26 |
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