Condensed-Gradient Boosting
This paper presents a computationally efficient variant of Gradient Boosting (GB) for multi-class classification and multi-output regression tasks. Standard GB uses a 1-vs-all strategy for classification tasks with more than two classes. This strategy entails that one tree per class and iteration ha...
| Autores: | , |
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| Formato: | artículo |
| Fecha de publicación: | 2024 |
| País: | España |
| Recursos: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/714256 |
| Acesso em linha: | http://hdl.handle.net/10486/714256 https://dx.doi.org/10.1007/s13042-024-02279-0 |
| Access Level: | acceso abierto |
| Palavra-chave: | Gradient Boosting Machine Multi-Class Classification Multi-Output Regression Informática |
| Resumo: | This paper presents a computationally efficient variant of Gradient Boosting (GB) for multi-class classification and multi-output regression tasks. Standard GB uses a 1-vs-all strategy for classification tasks with more than two classes. This strategy entails that one tree per class and iteration has to be trained. In this work, we propose the use of multi-output regressors as base models to handle the multi-class problem as a single task. In addition, the proposed modification allows the model to learn multi-output regression problems. An extensive comparison with other multi-output based Gradient Boosting methods is carried out in terms of generalization and computational efficiency. The proposed method showed the best trade-off between generalization ability and training and prediction speeds. Furthermore, an analysis of space and time complexity was undertaken |
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