A novel measure for evaluating classifiers
Expert Systems with Applications: An International Journal
The ROC skeleton for multiclass ROC estimation
Pattern Recognition Letters
Sensitivity versus accuracy in multiclass problems using memetic Pareto evolutionary neural networks
IEEE Transactions on Neural Networks
Imputing missing values in nuclear safeguards evaluation by a 2-tuple computational model
ICAISC'10 Proceedings of the 10th international conference on Artificial intelligence and soft computing: Part I
IEEE Transactions on Evolutionary Computation
An online AUC formulation for binary classification
Pattern Recognition
A two-stage evolutionary algorithm based on sensitivity and accuracy for multi-class problems
Information Sciences: an International Journal
Feature selection for MAUC-oriented classification systems
Neurocomputing
Pacc - a discriminative and accuracy correlated measure for assessment of classification results
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
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ROC analysis has become a standard tool in the design and evaluation of 2-class classification problems. It allows for an analysis that incorporates all possible priors, costs, and operating points, which is important in many real problems, where conditions are often nonideal. Extending this to the multiclass case is attractive, conferring the benefits of ROC analysis to a multitude of new problems. Even though ROC analysis does extend theoretically to the multiclass case, the exponential computational complexity as a function of the number of classes is restrictive. In this paper we show that the multiclass ROC can often be simplified considerably because some ROC dimensions are independent of each other. We present an algorithm that analyses interactions between various ROC dimensions, identifying independent classes, and groups of interacting classes, allowing the ROC to be decomposed. The resultant decomposed ROC hypersurface can be interrogated in a similar fashion to the ideal case, allowing for approaches such as cost-sensitive and Neyman-Pearson optimisation, as well as the volume under the ROC. An extensive bouquet of examples and experiments demonstrates the potential of this methodology.