Abstract
The combination of classifiers is an established technique to improve the classification performance. The combination rules proposed up to now generally try to decrease the classification error rate, which is a performance measure not suitable in many real situations and particularly when dealing with two class problems. In this case, a good alternative is given by the Area under the Receiver Operating Characteristic curve (AUC). This paper presents a method for the linear combination of two-class classifiers aimed at maximizing the AUC. The effectiveness of the approach has been confirmed by the tests performed on standard datasets.
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Keywords
- Receiver Operating Characteristic Curve
- Sequential Quadratic Programming
- Combination Rule
- Greedy Approach
- Classification Error Rate
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References
Fumera, G., Roli, F.: A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems. IEEE Trans. on Patt. Anal. and Mach. Intell. 27(6), 942–956 (2005)
Provost, F., Fawcett, T., Kohavi, R.: The Case against Accuracy Estimation for Comparing Induction Algorithms. In: Proc. ICML 1998, pp. 445–453. Morgan Kaufmann, San Francisco (1998)
Huang, J., Ling, C.X.: Using AUC and Accuracy in Evaluating Learning Algorithms. IEEE Transactions on Knowledge and Data Engineering 17, 299–310 (2005)
Webb, A.: Statistical pattern Recognition, 2nd edn. Wiley, USA (2002)
Pepe, M.: The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford University Press, Oxford, UK (2003)
Lehmann, E.L.: Nonparametrics. In: Statistical Methods Based on Ranks. Holden Day, S. Francisco (1975)
Hanley, J.A., McNeil, B.J.: The Meaning and the Use of the Area under a Receiver Operating Characteristic Curve. Radiology 143, 29–36 (1982)
Blake, C., Keogh, E., Merz, C.J.: UCI Repository of Machine Learning Databases (1998), www.ics.uci.edu/~mlearn/MLRepository.html
Joachims, T.: Making Large-Scale SVM Learning Practical. In: Schölkopf, B., Burges, C.J.C., Smola, A.J. (eds.) Advances in Kernel Methods, pp. 169–184. MIT Press, Cambridge (1999)
Flake, G.W., Pearlmuter, B.A.: Differentiating Functions of the Jacobian with Respect to the Weights. In: Solla, S.A., Leen, T.K., Müller, K. (eds.) Advances in Neural Information Processing Systems, vol. 12. The MIT Press, Cambridge (2000)
Huyer, W., Neumaier, A.: Global optimization by multilevel coordinate search. J. Global Optimization 14, 331–355 (1999)
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© 2006 Springer-Verlag Berlin Heidelberg
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Marrocco, C., Molinara, M., Tortorella, F. (2006). AUC-Based Linear Combination of Dichotomizers. In: Yeung, DY., Kwok, J.T., Fred, A., Roli, F., de Ridder, D. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2006. Lecture Notes in Computer Science, vol 4109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11815921_78
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DOI: https://doi.org/10.1007/11815921_78
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37236-3
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