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GRANDE: Gradient-Based Decision Tree Ensembles for tabular data
Marton, Sascha
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Lüdtke, Stefan
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Bartelt, Christian
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Stuckenschmidt, Heiner
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URL:
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https://iclr.cc/virtual/2024/poster/18457
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Additional URL:
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https://openreview.net/forum?id=XEFWBxi075
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Document Type:
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Conference or workshop publication
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Year of publication:
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2024
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Book title:
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International Conference on Learning Representations
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Page range:
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1-27
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Conference title:
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TheTwelfth International Conference on Learning Representations, ICLR 2024
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Location of the conference venue:
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Wien, Austria
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Date of the conference:
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07.-11.05.2024
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Publishing house:
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OpenReview.net
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Related URLs:
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Publication language:
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English
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Institution:
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School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
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Subject:
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004 Computer science, internet
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Keywords (English):
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tabular data , architectures . ensembles , decision trees , gradient descent
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Abstract:
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Despite the success of deep learning for text and image data, tree-based ensemble models are still state-of-the-art for machine learning with heterogeneous tabular data. However, there is a significant need for tabular-specific gradient-based methods due to their high flexibility. In this paper, we propose GRANDE, GRAdieNt-Based Decision Tree Ensembles, a novel approach for learning hard, axis-aligned decision tree ensembles using end-to-end gradient descent. GRANDE is based on a dense representation of tree ensembles, which affords to use backpropagation with a straight-through operator to jointly optimize all model parameters. Our method combines axis-aligned splits, which is a useful inductive bias for tabular data, with the flexibility of gradient-based optimization. Furthermore, we introduce an advanced instance-wise weighting that facilitates learning representations for both, simple and complex relations, within a single model. We conducted an extensive evaluation on a predefined benchmark with 19 classification datasets and demonstrate that our method outperforms existing gradient-boosting and deep learning frameworks on most datasets. The method is available under: https://github.com/s-marton/GRANDE
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 | Dieser Eintrag ist Teil der Universitätsbibliographie. |
 Search Authors in
BASE:
Marton, Sascha
;
Lüdtke, Stefan
;
Bartelt, Christian
;
Stuckenschmidt, Heiner
Google Scholar:
Marton, Sascha
;
Lüdtke, Stefan
;
Bartelt, Christian
;
Stuckenschmidt, Heiner
ORCID:
Marton, Sascha  ORCID: 0000-0001-8151-9223 ; Lüdtke, Stefan ; Bartelt, Christian ; Stuckenschmidt, Heiner ORCID: 0000-0002-0209-3859
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