BoostNet-WB: Adaptive Boosting with Neural Network Weak Learners and Weighted Bootstrap Resampling
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| Natura: | Recurso digital |
| Lingua: | inglese |
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2025
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| _version_ | 1866902297888423936 |
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| author | Olshenbaum, Konstantin |
| author_facet | Olshenbaum, Konstantin |
| contents | <p><strong>BoostNet-WB</strong> is a practical modification of the AdaBoost algorithm that integrates compact neural networks as weak learners. Instead of classical reweighting, the method employs a weighted bootstrap resampling strategy to reflect example importance during stochastic gradient descent — without modifying standard loss functions or optimizers.</p> <p>The approach includes dynamically adjustable network architectures and regularization techniques to prevent overfitting while preserving weak learner behavior. Experimental results across synthetic datasets show that BoostNet-WB consistently outperforms classical decision stump-based AdaBoost in classification performance, and the adaptive version achieves a favorable trade-off between accuracy and training time.</p> <p>This work highlights the feasibility of neural networks in boosting frameworks and offers a foundation for future extensions, including deeper or convolutional architectures.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15948128 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | BoostNet-WB: Adaptive Boosting with Neural Network Weak Learners and Weighted Bootstrap Resampling Olshenbaum, Konstantin Supervised Machine Learning Machine Learning/classification Neural Networks, Computer ensemble learning AdaBoost bootstrap resampling weak learners stochastic gradient descent neural boosting <p><strong>BoostNet-WB</strong> is a practical modification of the AdaBoost algorithm that integrates compact neural networks as weak learners. Instead of classical reweighting, the method employs a weighted bootstrap resampling strategy to reflect example importance during stochastic gradient descent — without modifying standard loss functions or optimizers.</p> <p>The approach includes dynamically adjustable network architectures and regularization techniques to prevent overfitting while preserving weak learner behavior. Experimental results across synthetic datasets show that BoostNet-WB consistently outperforms classical decision stump-based AdaBoost in classification performance, and the adaptive version achieves a favorable trade-off between accuracy and training time.</p> <p>This work highlights the feasibility of neural networks in boosting frameworks and offers a foundation for future extensions, including deeper or convolutional architectures.</p> |
| title | BoostNet-WB: Adaptive Boosting with Neural Network Weak Learners and Weighted Bootstrap Resampling |
| topic | Supervised Machine Learning Machine Learning/classification Neural Networks, Computer ensemble learning AdaBoost bootstrap resampling weak learners stochastic gradient descent neural boosting |
| url | https://doi.org/10.5281/zenodo.15948128 |