On Imbalanced Regression with Hoeffding Trees
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866914621239066624 |
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| author | Alchirch, Pantia-Marina Diochnos, Dimitrios I. |
| author_facet | Alchirch, Pantia-Marina Diochnos, Dimitrios I. |
| contents | Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing tradition due to their effectiveness, either alone or as base models in broader ensembles. Recent batch-learning work shows that kernel density estimation (KDE) improves smoothed predictions in imbalanced regression [Yang et al., 2021], while hierarchical shrinkage (HS) provides post-hoc regularization for decision trees without modifying their structure [Agarwal et al., 2022]. We extend KDE to streaming settings via a telescoping formulation and integrate HS into incremental decision trees. Empirical evaluation on standard online regression benchmarks shows that KDE consistently improves early-stream performance, whereas HS provides limited gains. Our implementation is publicly available at: https://github.com/marinaAlchirch/DSFA_2026. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_22101 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | On Imbalanced Regression with Hoeffding Trees Alchirch, Pantia-Marina Diochnos, Dimitrios I. Machine Learning Artificial Intelligence Many real-world applications generate continuous data streams for regression. Hoeffding trees and their variants have a long-standing tradition due to their effectiveness, either alone or as base models in broader ensembles. Recent batch-learning work shows that kernel density estimation (KDE) improves smoothed predictions in imbalanced regression [Yang et al., 2021], while hierarchical shrinkage (HS) provides post-hoc regularization for decision trees without modifying their structure [Agarwal et al., 2022]. We extend KDE to streaming settings via a telescoping formulation and integrate HS into incremental decision trees. Empirical evaluation on standard online regression benchmarks shows that KDE consistently improves early-stream performance, whereas HS provides limited gains. Our implementation is publicly available at: https://github.com/marinaAlchirch/DSFA_2026. |
| title | On Imbalanced Regression with Hoeffding Trees |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2602.22101 |