Machine Learning
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Prediction of Ordinal Classes Using Regression Trees
ISMIS '00 Proceedings of the 12th International Symposium on Foundations of Intelligent Systems
Gaussian Processes for Ordinal Regression
The Journal of Machine Learning Research
Neural Computation
Support Vector Ordinal Regression
Neural Computation
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
Evaluation Measures for Ordinal Regression
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
Learning partial ordinal class memberships with kernel-based proportional odds models
Computational Statistics & Data Analysis
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Kernelizing the proportional odds model through the empirical kernel mapping
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
An n-spheres based synthetic data generator for supervised classification
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Can machine learning techniques help to improve the common fisheries policy?
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
An organ allocation system for liver transplantation based on ordinal regression
Applied Soft Computing
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In this paper, an experimental study of different ordinal regression methods and measures is presented. The first objective is to gather the results of a considerably high number of methods, datasets and measures, since there are not many previous comparative studies of this kind in the literature. The second objective is to detect the redundancy between the evaluation measures used for ordinal regression. The results obtained present the maximum MAE (maximum of the mean absolute error of the difference between the true and the predicted ranks of the worst classified class) as a very interesting alternative for ordinal regression, being the less uncorrelated with respect to the rest of measures. Additionally, SVOREX and SVORIM are found to yield very good performance when the objective is to minimize this maximum MAE.