Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Generalization Bounds for the Area Under the ROC Curve
The Journal of Machine Learning Research
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Prediction of Ordinal Classes Using Regression Trees
Fundamenta Informaticae - Intelligent Systems
On the scalability of ordered multi-class ROC analysis
Computational Statistics & Data Analysis
Classifying carpets based on laser scanner data
Engineering Applications of Artificial Intelligence
Generalization Bounds for Some Ordinal Regression Algorithms
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Evaluation Methods for Ordinal Classification
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Binary Decomposition Methods for Multipartite Ranking
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Block-quantized support vector ordinal regression
IEEE Transactions on Neural Networks
A transitivity analysis of bipartite rankings in pairwise multi-class classification
Information Sciences: an International Journal
Adapting decision DAGs for multipartite ranking
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Computational Statistics & Data Analysis
On the ERA ranking representability of pairwise bipartite ranking functions
Artificial Intelligence
Training linear ranking SVMs in linearithmic time using red-black trees
Pattern Recognition Letters
Quantitative error measures for edge detection
Pattern Recognition
Multiscale edge detection based on Gaussian smoothing and edge tracking
Knowledge-Based Systems
On the impact of anisotropic diffusion on edge detection
Pattern Recognition
| Hi-index | 0.10 |
Nowadays the area under the receiver operating characteristics (ROC) curve, which corresponds to the Wilcoxon-Mann-Whitney test statistic, is increasingly used as a performance measure for binary classification systems. In this article we present a natural generalization of this concept for more than two ordered categories, a setting known as ordinal regression. Our extension of the Wilcoxon-Mann-Whitney statistic now corresponds to the volume under an r-dimensional surface (VUS) for r ordered categories and differs from extensions recently proposed for multi-class classification. VUS rather evaluates the ranking returned by an ordinal regression model instead of measuring the error rate, a way of thinking which has especially advantages with skew class or cost distributions. We give theoretical and experimental evidence of the advantages and different behavior of VUS compared to error rate, mean absolute error and other ranking-based performance measures for ordinal regression. The results demonstrate that the models produced by ordinal regression algorithms minimizing the error rate or a preference learning based loss, not necessarily impose a good ranking on the data.