Robust Classification for Imprecise Environments
Machine Learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
The mean subjective utility score, a novel metric for cost-sensitive classifier evaluation
Pattern Recognition Letters
Confidence-based classifier design
Pattern Recognition
Approximating the multiclass ROC by pairwise analysis
Pattern Recognition Letters
On reoptimizing multi-class classifiers
Machine Learning
Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Multiclass operating characteristics are a generalisation of the two-class receiver operator characteristic. A limitation regarding this generalisation is the computational complexity with increasing numbers of classes. In this paper, the ROC skeleton approach is proposed for efficiently estimating the operating characteristic. New operating points are computed from actual training samples, versus an alternative approach involving grid generation, that is prone to redundant calculations, and poor adaptation to certain classifier architectures. An extensive experimentation with a number of datasets and classifiers as a function of the number of calculations reveals the efficiency of this approach. Also notable is how in many cases good performance can be achieved with surprisingly few calculations, but the converse may also apply.