A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
An efficient boosting algorithm for combining preferences
The Journal of Machine Learning Research
New approaches to support vector ordinal regression
ICML '05 Proceedings of the 22nd international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Large-Margin thresholded ensembles for ordinal regression: theory and practice
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Additive Regression Applied to a Large-Scale Collaborative Filtering Problem
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Rough Set Approach to Knowledge Discovery about Preferences
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Cascade generalisation for ordinal problems
International Journal of Artificial Intelligence and Soft Computing
Monotone instance ranking with MIRA
DS'11 Proceedings of the 14th international conference on Discovery science
Adaptive metric learning vector quantization for ordinal classification
Neural Computation
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We consider the problem of ordinal classification, in which a value set of the decision attribute (output, dependent variable) is finite and ordered. This problem shares some characteristics of multi-class classification and regression, however, in contrast to the former, the order between class labels cannot be neglected, and, in the contrast to the latter, the scale of the decision attribute is not cardinal. In the paper, following the theoretical framework for ordinal classification, we introduce two algorithms based on gradient descent approach for learning ensemble of base classifiers being decision rules. The learning is performed by greedy minimization of so-called threshold loss, using a forward stagewise additive modeling. Experimental results are given that demonstrate the usefulness of the approach.