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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.15314 |
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| _version_ | 1866912999544979456 |
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| author | Tunç, Hüseyin Özese, Doğanay Birbil, Ş. İlker Maragno, Donato Caserta, Marco Baydoğan, Mustafa |
| author_facet | Tunç, Hüseyin Özese, Doğanay Birbil, Ş. İlker Maragno, Donato Caserta, Marco Baydoğan, Mustafa |
| contents | Incorporating domain-specific constraints into machine learning models is essential for generating predictions that are both accurate and feasible in real-world applications. This paper introduces new methods for training Output-Constrained Regression Trees (OCRT), addressing the limitations of traditional decision trees in constrained multi-target regression tasks. We propose three approaches: M-OCRT, which uses split-based mixed integer programming to enforce constraints; E-OCRT, which employs an exhaustive search for optimal splits and solves constrained prediction problems at each decision node; and EP-OCRT, which applies post-hoc constrained optimization to tree predictions. To illustrate their potential uses in ensemble learning, we also introduce a random forest framework working under convex feasible sets. We validate the proposed methods through a computational study both on synthetic and industry-driven hierarchical time series datasets. Our results demonstrate that imposing constraints on decision tree training results in accurate and feasible predictions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_15314 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Output-Constrained Decision Trees Tunç, Hüseyin Özese, Doğanay Birbil, Ş. İlker Maragno, Donato Caserta, Marco Baydoğan, Mustafa Machine Learning Incorporating domain-specific constraints into machine learning models is essential for generating predictions that are both accurate and feasible in real-world applications. This paper introduces new methods for training Output-Constrained Regression Trees (OCRT), addressing the limitations of traditional decision trees in constrained multi-target regression tasks. We propose three approaches: M-OCRT, which uses split-based mixed integer programming to enforce constraints; E-OCRT, which employs an exhaustive search for optimal splits and solves constrained prediction problems at each decision node; and EP-OCRT, which applies post-hoc constrained optimization to tree predictions. To illustrate their potential uses in ensemble learning, we also introduce a random forest framework working under convex feasible sets. We validate the proposed methods through a computational study both on synthetic and industry-driven hierarchical time series datasets. Our results demonstrate that imposing constraints on decision tree training results in accurate and feasible predictions. |
| title | Output-Constrained Decision Trees |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2405.15314 |