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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dembczyński, K., Waegeman, W., Cheng, W., Hüllermeier, E.: An exact algorithm for f-measure maximization. Advances in Neural Information Processing Systems 24, 223–230 (2011)
Al-Haddad, L., Morris, C.W., Boddy, L.: Training radial basis function neural networks: effects of training set size and imbalanced training sets. J. of Microbiological Methods 43(1), 33–44 (2000)
Bilmes, J., Asanovic, K., Chin, C.W., Demmel, J.: Using PHiPAC to speed error back-propagation learning. In: Proc. of ICASSP, vol. 5, pp. 4153–4156 (1997)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley (2001)
Gatos, B., Ntirogiannis, K., Pratikakis, I.: ICDAR 2009 document image binarization contest (DIBCO 2009). In: Proc. of ICDAR, pp. 1375–1382 (2009)
Gatos, B., Ntirogiannis, K., Pratikakis, I.: DIBCO 2009: document image binarization contest. Int. J. on Document Analysis and Recognition 14(1), 35–44 (2011)
Hidalgo, J.L., España, S., Castro, M.J., Pérez, J.A.: Enhancement and cleaning of handwritten data by using neural networks. In: Marques, J.S., Pérez de la Blanca, N., Pina, P. (eds.) IbPRIA 2005. LNCS, vol. 3522, pp. 376–383. Springer, Heidelberg (2005)
Jansche, M.: Maximum expected f-measure training of logistic regression models. In: Proc. of HLT & EMNLP, pp. 692–699 (2005)
Musicant, D.R., Kumar, V., Ozgur, A.: Optimizing f-measure with support vector machines. In: Proc. of Int. Florida AI Research Society Conference, pp. 356–360 (2003)
Ntirogiannis, K., Gatos, B., Pratikakis, I.: A Performance Evaluation Methodology for Historical Document Image Binarization (2012)
Pratikakis, I., Gatos, B., Ntirogiannis, K.: ICFHR 2012 Competition on Handwritten Document Image Binarization (H-DIBCO 2012) (2012)
Pratikakis, I., Gatos, B., Ntirogiannis, K.: H-DIBCO 2010-handwritten document image binarization competition. In: Proc. of ICFHR, pp. 727–732 (2010)
van Rijsbergen, C.J.: A theoretical basis for the use of co-occurrence data in information retrieval. J. of Documentation 33(2), 106–119 (1977)
Wolf, C.: Document Ink Bleed-Through Removal with Two Hidden Markov Random Fields and a Single Observation Field. IEEE PAMI 32(3), 431–447 (2010)
Zhou, Z.H., Liu, X.Y.: Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Trans. on Knowledge and Data Engineering 18(1), 63–77 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pastor-Pellicer, J., Zamora-Martínez, F., España-Boquera, S., Castro-Bleda, M.J. (2013). F-Measure as the Error Function to Train Neural Networks. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-38679-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38678-7
Online ISBN: 978-3-642-38679-4
eBook Packages: Computer ScienceComputer Science (R0)