F-measure as the error function to train neural networks

Authors:
Joan Pastor-Pellicer;Francisco Zamora-Martínez;Salvador España-Boquera;María José Castro-Bleda
Affiliations:
epartament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Valencia, Spain;Departamento de Ciencias Físicas, Matemáticas y de la Computación, Universidad CEU Cadenal Herrera, Alfara del Patriarca, Valencia, Spain;epartament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Valencia, Spain;epartament de Sistemes Informàtics i Computació, Universitat Politècnica de València, Valencia, Spain
Venue:
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Year:
2013

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Abstract

Imbalance datasets impose serious problems in machine learning. For many tasks characterized by imbalanced data, the F-Measure seems more appropiate than the Mean Square Error or other error measures. This paper studies the use of F-Measure as the training criterion for Neural Networks by integrating it in the Error-Backpropagation algorithm. This novel training criterion has been validated empirically on a real task for which F-Measure is typically applied to evaluate the quality. The task consists in cleaning and enhancing ancient document images which is performed, in this work, by means of neural filters.