Learning with Subset Stacking
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2021
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| Subjects: | |
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| _version_ | 1866915798559227904 |
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| author | Birbil, Ş. İlker Yıldırım, Sinan Çopur, Samet Akyüz, M. Hakan |
| author_facet | Birbil, Ş. İlker Yıldırım, Sinan Çopur, Samet Akyüz, M. Hakan |
| contents | We propose a new regression algorithm that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the output variable exhibits a heterogeneous behavior across the predictor space. The algorithm starts with generating subsets that are concentrated around random points in the input space. This is followed by training a local predictor for each subset. Those predictors are then combined in a novel way to yield an overall predictor. We call this algorithm "LEarning with Subset Stacking" or LESS, due to its resemblance to the method of stacking regressors. We offer bagging and boosting variants of LESS and test against the state-of-the-art methods on several datasets. Our comparison shows that LESS is highly competitive. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2112_06251 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Learning with Subset Stacking Birbil, Ş. İlker Yıldırım, Sinan Çopur, Samet Akyüz, M. Hakan Machine Learning We propose a new regression algorithm that learns from a set of input-output pairs. Our algorithm is designed for populations where the relation between the input variables and the output variable exhibits a heterogeneous behavior across the predictor space. The algorithm starts with generating subsets that are concentrated around random points in the input space. This is followed by training a local predictor for each subset. Those predictors are then combined in a novel way to yield an overall predictor. We call this algorithm "LEarning with Subset Stacking" or LESS, due to its resemblance to the method of stacking regressors. We offer bagging and boosting variants of LESS and test against the state-of-the-art methods on several datasets. Our comparison shows that LESS is highly competitive. |
| title | Learning with Subset Stacking |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2112.06251 |