Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature Interactions Article Swipe
Revista, Zen
,
IA, 10
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17819445
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17819445
Decision trees are fundamental machine learning models known for their interpretability and efficiency. However, standard decision tree algorithms, such as CART and C4.5, predominantly rely on axis-aligned splits, which limit their ability to capture complex feature interactions effectively. This paper introduces a novel approach to decision tree induction that transcends the limitations of axis-aligned splits by employing learnable splitting functions. Our method learns splitting criteria directly from the data, enabling the trees to capture non-linear relationships and intricate feature combinations. We propose a differentiable decision tree framework that uses gradient-based optimization to train the splitting parameters. This framework allows us to seamlessly integrate decision tree learning with other deep learning architectures. We demonstrate the effectiveness of our approach through extensive experiments on various benchmark datasets, showing significant improvements in accuracy and model complexity compared to traditional decision tree algorithms. Furthermore, we analyze the learned splitting functions to provide insights into the feature interactions captured by the model. Our findings highlight the potential of learnable decision trees to address complex classification and regression problems, offering a powerful alternative to existing methods.
Related Topics
Concepts
Interpretability
Decision tree
Artificial intelligence
Feature (linguistics)
Machine learning
Computer science
Incremental decision tree
Decision tree learning
Tree (set theory)
Benchmark (surveying)
Alternating decision tree
Decision tree model
ID3 algorithm
Decision stump
Limit (mathematics)
Data mining
Tree structure
Mathematics
Logistic model tree
Decision problem
Feature selection
Metadata
- Type
- article
- Landing Page
- https://doi.org/10.5281/zenodo.17819445
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7108750104
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W7108750104Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17819445Digital Object Identifier
- Title
-
Beyond Axis-Aligned Splits: Learnable Decision Tree Inductions for Complex Feature InteractionsWork title
- Type
-
articleOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-12-04Full publication date if available
- Authors
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Revista, Zen, IA, 10List of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17819445Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17819445Direct OA link when available
- Concepts
-
Interpretability, Decision tree, Artificial intelligence, Feature (linguistics), Machine learning, Computer science, Incremental decision tree, Decision tree learning, Tree (set theory), Benchmark (surveying), Alternating decision tree, Decision tree model, ID3 algorithm, Decision stump, Limit (mathematics), Data mining, Tree structure, Mathematics, Logistic model tree, Decision problem, Feature selectionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
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