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GSVM: An SVM for handling imbalanced accuracy between classes in bi-classification problems

Autor
Gonzalez-Abril, L.; Núñez, H.; Angulo, C.; Velasco, F.
Tipus d'activitat
Article en revista
Revista
Applied soft computing
Data de publicació
2014-04
Volum
17
Pàgina inicial
23
Pàgina final
31
DOI
https://doi.org/10.1016/j.asoc.2013.12.013 Obrir en finestra nova
Projecte finançador
TRATAMIENTO DEL DOLOR Y LA ANSIEDAD BASADO EN LA INTERACCION DE ROBOTS SOCIALES CON
URL
http://www.sciencedirect.com/science/article/pii/S1568494613004420 Obrir en finestra nova
Resum
A new support vector machine, SVM, is introduced, called GSVM, which is specially designed for bi-classification problems where balanced accuracy between classes is the objective. Starting from a standard SVM, the GSVM is obtained from a low-cost post-processing strategy by modifying the initial bias. Thus, the bias for GSVM is calculated by moving the original bias in the SVM to improve the geometric mean between the true positive rate and the true negative rate. The proposed solution neither m...
Paraules clau
Classification, Cost-sensitive schemes, Imbalanced accuracy, Imbalanced databases, Support vector machines
Grup de recerca
GREC - Grup de Recerca en Enginyeria del Coneixement
IDEAI-UPC Intelligent Data Science and Artificial Intelligence

Participants

  • Gonzalez Abril, Luis  (autor)
  • Núñez Castro, Haydemar  (autor)
  • Angulo Bahón, Cecilio  (autor)
  • Velasco Morente, Francisco  (autor)