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GradTree: Learning axis-aligned decision trees with gradient descent
Marton, Sascha
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Lüdtke, Stefan
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Bartelt, Christian
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Stuckenschmidt, Heiner
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DOI:
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https://doi.org/10.1609/aaai.v38i13.29345
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URL:
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https://ojs.aaai.org/index.php/AAAI/article/view/2...
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Dokumenttyp:
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Konferenzveröffentlichung
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Erscheinungsjahr:
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2024
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Buchtitel:
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Proceedings of the 38th AAAI Conference on Artificial Intelligence
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Band/Volume:
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38, No. 13
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Seitenbereich:
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14323-14331
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Veranstaltungstitel:
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The 38th Annual AAAI Conference on Artificial Intelligence
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Veranstaltungsort:
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Vancouver, Canada
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Veranstaltungsdatum:
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20.-27.02.2024
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Herausgeber:
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Wooldridge, Michael
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Dy, Jennifer
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Natarajan, Sriraam
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Ort der Veröffentlichung:
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Washington, DC
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Verlag:
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AAAI Press
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ISBN:
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978-1-57735-887-9 , 1-57735-887-2
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ISSN:
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2159-5399
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Verwandte URLs:
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Sprache der Veröffentlichung:
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Englisch
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Einrichtung:
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Fakultät für Wirtschaftsinformatik und Wirtschaftsmathematik > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
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Fachgebiet:
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004 Informatik
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Freie Schlagwörter (Englisch):
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decision trees , gradient descent , machine learning , Artificial Intelligence
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Abstract:
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Decision Trees (DTs) are commonly used for many machine learning tasks due to their high degree of interpretability. However, learning a DT from data is a difficult optimization problem, as it is non-convex and non-differentiable. Therefore, common approaches learn DTs using a greedy growth algorithm that minimizes the impurity locally at each internal node. Unfortunately, this greedy procedure can lead to inaccurate trees.
In this paper, we present a novel approach for learning hard, axis-aligned DTs with gradient descent. The proposed method uses backpropagation with a straight-through operator on a dense DT representation, to jointly optimize all tree parameters. Our approach outperforms existing methods on binary classification benchmarks and achieves competitive results for multi-class tasks. The implementation is available under: https://github.com/s-marton/GradTree
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Suche Autoren in
BASE:
Marton, Sascha
;
Lüdtke, Stefan
;
Bartelt, Christian
;
Stuckenschmidt, Heiner
Google Scholar:
Marton, Sascha
;
Lüdtke, Stefan
;
Bartelt, Christian
;
Stuckenschmidt, Heiner
ORCID:
Marton, Sascha  ORCID: https://orcid.org/0000-0001-8151-9223, Lüdtke, Stefan, Bartelt, Christian and Stuckenschmidt, Heiner ORCID: https://orcid.org/0000-0002-0209-3859
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