Unified Performance Measure for Binary Classification Problems

• Redondo, Ana R. ^[1] ; Jorge Navarro ^[1] ; Fernández, Rubén R. ^[1] ; Martín de Diego, Isaac ^[1] ; Moguerza, Javier M. ^[1] ; Juan José Fernández-Muñoz ^[1]
  1. [1] Universidad Rey Juan Carlos

     Universidad Rey Juan Carlos

     Madrid, España

     
• Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.) , David Camacho Fernández (ed. lit.) , Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 104-112
• Idioma: inglés
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• Resumen

  • Different performance measures are used to inspect, compare and evaluate the behaviour of classifiers in Machine Learning (ML). ML researchers employ one or several of these performance measures in their classification studies to report their success. However, no widespread consensus has been reached on a unified chosen measure. In this work, we introduce a reliable and informative measure, the Unified Performance Measure (UPM), by modifying the F1-score to avoid its undesired behaviour in imbalanced classification problems. UPM is compared with alternative performance measures, like the F1-score or Accuracy, in both simulated confusion matrices and real datasets. The proposed measure outperforms the alternatives, providing a promising new research line.