Random effects in ordinal regression models
Computational Statistics & Data Analysis
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
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Neural Networks - 2005 Special issue: IJCNN 2005
Support Vector Ordinal Regression
Neural Computation
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
The unimodal model for the classification of ordinal data
Neural Networks
Evaluation Measures for Ordinal Regression
ISDA '09 Proceedings of the 2009 Ninth International Conference on Intelligent Systems Design and Applications
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This paper presents a novel method for generally adapting ordinal classification models. We essentially rely on the assumption that the ordinal structure of the set of class labels is also reflected in the topology of the instance space. Under this assumption, this paper proposes an algorithm in two phases that takes advantage of the ordinal structure of the dataset and tries to translate this ordinal structure in the total ordered real line and then to rank the patterns of the dataset. The first phase makes a projection of the ordinal structure of the feature space. Next, an evolutionary algorithm tunes the first projection working with the misclassified patterns near the border of their right class. The results obtained in seven ordinal datasets are competitive in comparison with state-of-the-art algorithms in ordinal regression, but with much less computational time in datasets with many patterns.