GradTree: Learning axis-aligned decision trees with gradient descent


Marton, Sascha ; Lüdtke, Stefan ; Bartelt, Christian ; Stuckenschmidt, Heiner


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URL: https://underline.io/events/439/sessions/17378/lec...
URN: urn:nbn:de:bsz:180-madoc-670259
Document Type: Conference presentation
Year of publication: 2024
Conference title: The 38th Annual AAAI Conference on Artificial Intelligence
Location of the conference venue: Vancouver, Canada
Date of the conference: 20.-27.02.2024
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Practical Computer Science II: Artificial Intelligence (Stuckenschmidt 2009-)
Außerfakultäre Einrichtungen > Institut für Enterprise Systems (InES)
License: CC BY 4.0 Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Abstract: 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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