Abstract
Binary classifiers are used in many complex classification problems in which the classification result could have serious consequences. Thus, they should ensure a very high reliability to avoid erroneous decisions. Unfortunately, this is rarely the case in real situations where the cost for a wrong classification could be so high that it should be convenient to reject the sample which gives raise to an unreliable result. However, as far as we know, a reject option specifically devised for binary classifiers has not been yet proposed. This paper presents an optimal reject rule for binary classifiers, based on the Receiver Operating Characteristic curve. The rule is optimal since it maximizes a classification utility function, defined on the basis of classification and error costs peculiar for the application at hand. Experiments performed with a data set publicly available confirmed the effectiveness of the proposed reject rule.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Chow, C.K.: An Optimum Character Recognition System Using Decision Functions. IRE Trans. Electronic Computers EC-6 (1957) 247–254
Chow, C.K.: On Optimum Recognition Error and Reject Tradeoff. IEEE Trans. Inf. Th. IT-10 (1970) 41–46
Dubuisson, B., Masson, M.: A Statistical Decision Rule with Incomplete Knowledge about Classes. Pattern Recognition 26 (1993) 155–165
Muzzolini, R., Yang, Y.-H., Pierson, R.: Classifier Design with Incomplete Knowledge. Pattern Recognition 31 (1998) 345–369
Cordella, L.P., De Stefano, C., Tortorella, F., Vento, M.: A Method for Improving Classification Reliability of Multilayer Perceptrons. IEEE Trans. Neur. Net. 6 (1995) 1140–1147
Bradley, A.P.: The use of the Area under the ROC Curve in the Evaluation of Machine Learning Algorithms. Pattern Recognition 30 (1997) 1145–1159
Provost, F., Fawcett, T.: Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions. Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining (KDD-97)
Blake, C., Keogh, E., Merz, C.J.: UCI Repository of machine learning databases, [http://www.ics.uci.edu/~mlearn/MLRepository.html] Irvine, CA: University of California, Department of Information and Computer Science, 1998
Hoekstra, A., Kraaijved, M.A., de Ridder, D., Schmidt, W.F., Ypma, A.: The complete SPRLIB & ANNLIB. Statistical Pattern recognition and Artificial Neural Network Library. 2nd edn. Version 3.1. User’s Guide and Reference Manual, Pattern Recognition Group, Faculty of Applied Physics, Delft University of Technology, Delft, The Netherlands (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tortorella, F. (2000). An Optimal Reject Rule for Binary Classifiers. In: Ferri, F.J., Iñesta, J.M., Amin, A., Pudil, P. (eds) Advances in Pattern Recognition. SSPR /SPR 2000. Lecture Notes in Computer Science, vol 1876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44522-6_63
Download citation
DOI: https://doi.org/10.1007/3-540-44522-6_63
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67946-2
Online ISBN: 978-3-540-44522-7
eBook Packages: Springer Book Archive