Learning to Predict One or More Ranks in Ordinal Regression Tasks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Generalization Bounds for Some Ordinal Regression Algorithms
ALT '08 Proceedings of the 19th international conference on Algorithmic Learning Theory
Efficient Decoding of Ternary Error-Correcting Output Codes for Multiclass Classification
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Binary Decomposition Methods for Multipartite Ranking
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
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
Ordinal-class core vector machine
Journal of Computer Science and Technology
On exploiting hierarchical label structure with pairwise classifiers
ACM SIGKDD Explorations Newsletter
Diagnostic of pathology on the vertebral column with embedded reject option
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Efficient prediction algorithms for binary decomposition techniques
Data Mining and Knowledge Discovery
Towards learning spiculation score of the masses in mammography images
IWDM'10 Proceedings of the 10th international conference on Digital Mammography
Large-margin feature selection for monotonic classification
Knowledge-Based Systems
An experimental study of different ordinal regression methods and measures
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Evolutionary extreme learning machine for ordinal regression
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
Ordinal and nominal classification of wind speed from synoptic pressurepatterns
Engineering Applications of Artificial Intelligence
Exploitation of pairwise class distances for ordinal classification
Neural Computation
An organ allocation system for liver transplantation based on ordinal regression
Applied Soft Computing
The data replication method for the classification with reject option
AI Communications
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Classification of ordinal data is one of the most important tasks of relation learning. This paper introduces a new machine learning paradigm specifically intended for classification problems where the classes have a natural order. The technique reduces the problem of classifying ordered classes to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Generalization bounds of the proposed ordinal classifier are also provided. An experimental study with artificial and real data sets, including an application to gene expression analysis, verifies the usefulness of the proposed approach.