Previous |  Up |  Next

Article

Title: Relative cost curves: An alternative to AUC and an extension to 3-class problems (English)
Author: Montvida, Olga
Author: Klawonn, Frank
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 50
Issue: 5
Year: 2014
Pages: 647-660
Summary lang: English
.
Category: math
.
Summary: Performance evaluation of classifiers is a crucial step for selecting the best classifier or the best set of parameters for a classifier. Receiver Operating Characteristic (ROC) curves and Area Under the ROC Curve (AUC) are widely used to analyse performance of a classifier. However, the approach does not take into account that misclassification for different classes might have more or less serious consequences. On the other hand, it is often difficult to specify exactly the consequences or costs of misclassifications. This paper is devoted to Relative Cost Curves (RCC) - a graphical technique for visualising the performance of binary classifiers over the full range of possible relative misclassification costs. This curve provides helpful information to choose the best set of classifiers or to estimate misclassification costs if those are not known precisely. In this paper, the concept of Area Above the RCC (AAC) is introduced, a scalar measure of classifier performance under unequal misclassification costs problem. We also extend RCC to multicategory problems when misclassification costs depend only on the true class. (English)
Keyword: classifier
Keyword: performance evaluation
Keyword: misclassification costs
Keyword: cost curves
Keyword: ROC curves
Keyword: AUC
MSC: 62A10
MSC: 62N05
MSC: 93E12
idZBL: Zbl 1305.93195
idMR: MR3301852
DOI: 10.14736/kyb-2014-5-0647
.
Date available: 2015-01-13T09:15:05Z
Last updated: 2016-01-03
Stable URL: http://hdl.handle.net/10338.dmlcz/144098
.
Reference: [1] 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. 10.1016/S0031-3203(96)00142-2
Reference: [2] Drummond, C., Holte, R. C.: Cost curves: An improved method for visualizing classifier performance..Machine Learning 65 (2006) 95-130. 10.1007/s10994-006-8199-5
Reference: [3] Fawcett, T.: An introduction to roc analysis..Pattern Recognition Lett. 27 (2006), 861-874. 10.1016/j.patrec.2005.10.010
Reference: [4] Hand, D. J.: Measuring classifier performance: a coherent alternative to the area under the ROC curve..Machine Learning 77 (2009), 103-123. 10.1007/s10994-009-5119-5
Reference: [5] Hand, D. J., Till, R. J.: A simple generalisation of the area under the ROC curve for multiple class classification problems..Machine Learning 45 (2001), 171-186. Zbl 1007.68180, 10.1023/A:1010920819831
Reference: [6] Hanley, J. A.: Receiver operating characteristic (ROC) methodology: the state of the art..Critical Reviews in Diagnostic Imaging 29 (1989), 307-335.
Reference: [7] Hernández-Orallo, J., Flach, P., Ferri, C.: Brier curves: a new cost-based visualisation of classifier performance..In: Proc. 28th International Conference on Machine Learning (ICML-11) (L. Getoor and T. Scheffer, eds.), ACM, New York 2011, pp. 585-592.
Reference: [8] Klawonn, F., Höppner, F., May, S.: An alternative to ROC and AUC analysis of classifiers..In: Advances in Intelligent Data Analysis X, (J. Gama, E. Bradley, and J. Hollmén, eds.), Springer, Berlin 2011, p. 210-221.
Reference: [9] Krzanowski, W. J., Hand, D. J.: ROC Curves for Continuous data..Chapman and Hall, London 2009. Zbl 1288.62005, MR 2522628
Reference: [10] Li, J., Fine, J. P.: ROC analysis with multiple classes and multiple tests: methodology and its application in microarray studies..Biostatistics 9 (2008), 566-576. Zbl 1143.62083, 10.1093/biostatistics/kxm050
Reference: [11] Murphy, P. M., Aha, D. W.: Uci repository of machine learning databases..1992. Avaible: http://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).
Reference: [12] Mossman, D.: Three-way ROCs..Medical Decision Making 19 (1999), 78-89. 10.1177/0272989X9901900110
.

Files

Files Size Format View
Kybernetika_50-2014-5_2.pdf 422.0Kb application/pdf View/Open
Back to standard record
Partner of
EuDML logo