Guided Random Forest and its application to data approximation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2019
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| _version_ | 1866912525585481728 |
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| author | Gupta, Prashant Jindal, Aashi Jayadeva Sengupta, Debarka |
| author_facet | Gupta, Prashant Jindal, Aashi Jayadeva Sengupta, Debarka |
| contents | We present a new way of constructing an ensemble classifier, named the Guided Random Forest (GRAF) in the sequel. GRAF extends the idea of building oblique decision trees with localized partitioning to obtain a global partitioning. We show that global partitioning bridges the gap between decision trees and boosting algorithms. We empirically demonstrate that global partitioning reduces the generalization error bound. Results on 115 benchmark datasets show that GRAF yields comparable or better results on a majority of datasets. We also present a new way of approximating the datasets in the framework of random forests. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1909_00659 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Guided Random Forest and its application to data approximation Gupta, Prashant Jindal, Aashi Jayadeva Sengupta, Debarka Machine Learning We present a new way of constructing an ensemble classifier, named the Guided Random Forest (GRAF) in the sequel. GRAF extends the idea of building oblique decision trees with localized partitioning to obtain a global partitioning. We show that global partitioning bridges the gap between decision trees and boosting algorithms. We empirically demonstrate that global partitioning reduces the generalization error bound. Results on 115 benchmark datasets show that GRAF yields comparable or better results on a majority of datasets. We also present a new way of approximating the datasets in the framework of random forests. |
| title | Guided Random Forest and its application to data approximation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/1909.00659 |