Exploiting AUC for optimal linear combinations of dichotomizers

Pattern Recognition Letters

Volumn 27, Issue 8, 2006, Pages 900-907

  Marrocco, Claudio ^a   Molinara, Mario ^a   Tortorella, Francesco ^a  

^a UNIVERSITY OF CASSINO AND SOUTHERN LAZIO   (Italy)

Author keywords

AUC; Linear combiners; Multiple classifier systems; ROC curve; Two class classifiers

Indexed keywords

LINEAR SYSTEMS; OPTIMAL SYSTEMS; PATTERN RECOGNITION; PROBLEM SOLVING;

AUC; LINEAR COMBINERS; MULTIPLE CLASSIFIER SYSTEMS; ROC CURVE; TWO-CLASS CLASSIFIERS;

CLASSIFIERS;

EID: 33646590655     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.patrec.2005.10.014     Document Type: Article

References (18)

1. Blake, C., Keogh, E., Merz, C.J., 1998. UCI Repository of Machine Learning Databases. Available from: .
2. 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
3. Cortes, C., Mohri, M., 2003. AUC optimization vs. error rate minimization. Advances in Neural Information Processing Systems (NIPS 2003).
4. Fawcett, T., 2003. ROC graphs: Notes and practical considerations for data mining researchers. HP Labs Tech Report HPL-2003-4.
5. Flach, P.A., 2003. The geometry of ROC space: Understanding machine learning metrics through ROC isometrics. In: Proc. Internat. Conf. on Machine Learning (ICML-2003).
6. Flake G.W., and Pearlmuter B.A. Differentiating functions of the Jacobian with respect to the weights. In: Solla S.A., Leen T.K., and Müller K. (Eds). Advances in Neural Information Processing Systems vol. 12 (2000), The MIT Press
7. Freund Y., Iyer R., Schapire R., and Singer Y. An efficient boosting algorithm for combining preferences. J. Machine Learning Res. 4 (2003) 933-969
8. Hand D.J., and Till R.J. A simple generalization of the area under the ROC curve for multiple class classification problems. Machine Learning 45 (2001) 171-186
9. Hanley J.A., and McNeil B.J. The meaning and the use of the area under a receiver operating characteristic curve. Radiology 143 (1982) 29-36
10. Huang J., and Ling C.X. Using AUC and accuracy in evaluating learning algorithms. IEEE Trans. Knowledge Data Eng. 17 (2005) 299-310
11. Joachims T. Making large-scale SVM learning practical. In: Schölkopf B., Burges C.J.C., and Smola A.J. (Eds). Advances in Kernel Methods-Support Vector Learning (1999), MIT Press 169-184
12. Mann H.B., and Whitney D.R. On a test whether one of two random variable is stochastically larger than the other. Ann. Math. Statist. 18 (1947) 50-60
13. Provost F., and Fawcett T. Robust classification for imprecise environments. Machine Learning 42 (2001) 203-231
14. Provost F., Fawcett T., and Kohavi R. The case against accuracy estimation for comparing induction algorithms. Proc. Internat. Conf. on Machine Learning (ICML-1998) (1998), Morgan Kaufmann 445-453
15. Rakotomamonjy, A., 2004. Optimizing area under ROC curve with SVMs. Workshop on ROC Analysis in Artificial Intelligence.
16. Rosset, S., 2004. Model selection via the AUC. In: Proc. Internat. Conf. on Machine Learning (ICML-2004).
17. Tortorella F. A ROC-based reject rule for dichotomizers. Pattern Recognition Lett. 26 (2005) 167-180
18. Walpole R.E., Myers R.H., and Myers S.L. Probability and Statistics for Engineers and Scientists. sixth ed. (1998), Prentice Hall Int., London