Vote-boosting ensembles
Vote-boosting is a sequential ensemble learning method in which the individual classi ers are built on diferent weighted versions of the training data. To build a new classi er, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predi...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2018 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/692397 |
| Acceso en línea: | http://hdl.handle.net/10486/692397 https://dx.doi.org/10.1016/j.patcog.2018.05.022 |
| Access Level: | acceso abierto |
| Palabra clave: | Ensemble learning Boosting Uncertainty-based emphasis Robust classification Informática |
| Sumario: | Vote-boosting is a sequential ensemble learning method in which the individual classi ers are built on diferent weighted versions of the training data. To build a new classi er, the weight of each training instance is determined in terms of the degree of disagreement among the current ensemble predictions for that instance. For low class-label noise levels, especially when simple base learners are used, emphasis should be made on instances for which the disagreement rate is high. When more exible classi ers are used and as the noise level increases, the emphasis on these uncertain instances should be reduced. In fact, at suffciently high levels of class-label noise, the focus should be on instances on which the ensemble classi ers agree. The optimal type of emphasis can be automatically determined using cross-validation. An extensive empirical analysis using the beta distribution as emphasis function illustrates that vote-boosting is an effective method to generate ensembles that are both accurate and robust |
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