An optimal multiclass classifier design
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.
| 2016 | |
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Support vector machines Optimization Algorithm design and analysis Procesamiento de Señales |
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| Inglés | |
| Universidad de la República | |
| COLIBRI | |
| https://hdl.handle.net/20.500.12008/42713 | |
| Acceso abierto | |
| Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) |
| Sumario: | Trabajo presentado en 23rd International Conference on Pattern Recognition (ICPR), Cancun, México, 4-8 dic, 2016 |
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