Abstract
In recent years, classifier combination has been of great interest for the pattern recognition community as a method to improve classification performance. Several combination rules have been proposed based on maximizing the accuracy and the Area under the ROC curve (AUC). Taking into account that there are several applications which focus only on a part of the ROC curve, i.e. the one most relevant for the problem, we recently proposed a new algorithm aimed at finding the linear combination of dichotomizers which maximizes only the interesting part of the AUC. Since the algorithm uses a greedy approach, in this paper we define and evaluate some possible strategies which select the dichotomizers to combine at each step of the greedy approach. An experimental comparison is drawn on a multibiometric database.
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References
Huang, J., Ling, C.X.: Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. on Knowledge and Data Engineering 17, 299–310 (2005)
Cortes, C., Mohri, M.: AUC optimization vs. error rate minimization advances. In: Neural Information Processing Systems. MIT Press, Cambridge (2003)
Dodd, L.E., Pepe, M.S.: Partial AUC estimation and regression. Biometrics 59, 614–623 (2003)
Ricamato, M.T., Tortorella, F.: Combination of Dichotomizers for Maximizing the Partial Area under the ROC Curve. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds.) SSPR&SPR 2010. LNCS, vol. 6218, pp. 660–669. Springer, Heidelberg (2010)
Bradley, A.P.: The use of the area under the roc curve in the evaluation of machine learning algorithms. Patt. Recogn. 30, 1145–1159 (1997)
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143, 29–36 (1982)
Nandakumar, K., Dass, S.C., Jain, A.K.: Likelihood ratio-based biometric score fusion. IEEE Trans. on Patt. Anal. and Mach. Intell. 30, 342–347 (2008)
Kendall, M.G.: A new measure of rank correlation. Biometrika 30, 81–93 (1938)
Giacinto, G., Roli, F.: Design of effective neural network ensembles for image classification processes. Image and Vision Computing 19, 699–707 (2001)
Poh, N., Bengio, S.: Database, protocol and tools for evaluating score-level fusion algorithms in biometric authentication. Patt. Recogn. 39, 223–233 (2006)
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Ricamato, M.T., Molinara, M., Tortorella, F. (2011). Selection Strategies for pAUC-Based Combination of Dichotomizers. In: Sansone, C., Kittler, J., Roli, F. (eds) Multiple Classifier Systems. MCS 2011. Lecture Notes in Computer Science, vol 6713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21557-5_20
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DOI: https://doi.org/10.1007/978-3-642-21557-5_20
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