DESReg: Dynamic Ensemble Selection library for Regression tasks
Nowadays, regression is a very demanded predictive task to solve a wide range of problems belonging to different research and society areas. Examples of applications include industry, economic, medical and energy fields. Ensemble methodology works by merging the output obtained from a set of base me...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2024 |
| País: | España |
| Institución: | Universidad de Jaén |
| Repositorio: | RUJA. Repositorio Institucional de la Producción Científica de la Universidad de Jaén |
| OAI Identifier: | oai:ruja.ujaen.es:10953/6152 |
| Acceso en línea: | https://doi.org/10.1016/j.neucom.2024.127487 https://hdl.handle.net/10953/6152 |
| Access Level: | acceso abierto |
| Palabra clave: | Ensembles Regression 004 |
| Sumario: | Nowadays, regression is a very demanded predictive task to solve a wide range of problems belonging to different research and society areas. Examples of applications include industry, economic, medical and energy fields. Ensemble methodology works by merging the output obtained from a set of base methods (learners), achieving successful results in both classification and regression tasks. Traditional ensembles use the output of the whole set of base methods, in a static way, to obtain the result of the ensemble. However, latest studies show that dynamic selection of learners or even dynamic aggregation of their outputs produce better results. Methodologies that integrate these techniques are called dynamic ensembles or dynamic ensemble selection. Although the literature and tools to work with dynamic ensembles for classification tasks is abundant, for regression tasks these resources are scarcer. This paper aims to mitigate these shortcomings by presenting a library for the design, development and execution of dynamic ensembles for regression problems. Specifically, the Python software package DESReg is presented. This library allows us to access to the latest dynamic ensemble techniques in the field, standing out for its high configurability, its support for extending it with user-defined functions or its parallel computation capabilities. |
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