Título:
An optimal multiclass classifier design
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Autor:
Fiori, Marcelo
Di Martino, Matías
Fernández, Alicia
Di Martino, Matías
Fernández, Alicia
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Tipo de documento:
Ponencia
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Palabras clave:
Support vector machines
Optimization
Algorithm design and analysis
Optimization
Algorithm design and analysis
Descriptores:
Procesamiento de Señales
Año de publicación:
2016
Contenido:
Resumen:
The use of different evaluation measures for classification tasks have gained a significant amount of attention in the past decade, specially for those problems with multiple and imbalanced classes. However, the optimization of classifiers with respect to these measures is still heuristic, using ad-hoc rules with classical accuracy-optimized classifiers. We propose a classifier designed specifically to optimize one of the possible measures, namely, the so-called G-mean. Nevertheless, the technique is general, and it can be used to optimize generic evaluation measures. The optimization algorithm to train the classifier is described, and the numerical scheme is tested showing its usability and robustness. The code is publicly available, as well as the datasets used along this paper.
Descripción:
Trabajo presentado en 23rd International Conference on Pattern Recognition (ICPR), Cancun, México, 4-8 dic, 2016
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Citación:
Fiori, M, Di Martino, M, Fernández, A. "An optimal multiclass classifier design" Publicado en: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR), Cancun, Mexico, 4-8 dic, 2016, pp. 480-485, doi: 10.1109/ICPR.2016.7899680.
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Departamento académico:
Procesamiento de Señales
Grupo de investigación:
Tratamiento de Imágenes
Licencia:
Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0)
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| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| FDF16.pdf | — | 1.39 MB | Adobe PDF |