Abstract
Two class classifiers are used in many complex problems in which the classification results could have serious consequences. In such situations the cost for a wrong classification can be so high that can be convenient to avoid a decision and reject the sample. This paper presents a comparison between two different reject rules (the Chow’s and the ROC rule). In particular, the experiments show that the Chow’s rule is inappropriate when the estimates of the a posteriori probabilities are not reliable.
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References
Webb, A.R.: Statistical Pattern Recognition. John Wiley and Sons Ltd, West Sussex (2002)
Chow, C.K.: On optimum recognition error and reject tradeoff. IEEE Trans. Information Theory IT 10, 41–46 (1970)
Tortorella, F.: A ROC-based reject rule for dichotomizers. Pattern Recognition Letters 26, 167–180 (2005)
Santos-Pereira, C.M., Pires, A.M.: On optimal reject rules and ROC curves, Pattern Recognition Letters. Pattern Recognition Letters 26, 943–952 (2005)
Xie, J., Qiu, Z., Wu, J.: Bootstrap methods for reject rules of Fisher LDA. In: Proc. 18th Int. Conf. on Pattern Recognition, 3rd edn., pp. 425–428. IEEE Press, NJ (2006)
van Trees, H.L.: Detection, Estimation, and Modulation Theory. Wiley, New York (1968)
Provost, F., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42, 203–231 (2001)
Mukhopadhyay, N.: Probability and Statistical Inference. Marcel Dekker Inc., New York (2000)
Garthwaite, P.H., Jolliffe, I.T., Jones, B.: Statistical Inference, 2nd edn. Oxford University Press, Oxford (2002)
Tortorella, F.: Reducing the classification cost of support vector classifiers through an ROC-based reject rule. Pattern Analysis and Applications 7, 128–143 (2004)
Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases, Irvine, University of California, Department of Information and Computer Science (1998), http://www.ics.uci.edu/mlearn/MLRepository.html
Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms, IEEE Trans. Knowledgde and Data Engineering 17, 299–310 (2005)
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Marrocco, C., Molinara, M., Tortorella, F. (2007). An Empirical Comparison of Ideal and Empirical ROC-Based Reject Rules. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2007. Lecture Notes in Computer Science(), vol 4571. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73499-4_5
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DOI: https://doi.org/10.1007/978-3-540-73499-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73498-7
Online ISBN: 978-3-540-73499-4
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