Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Machine Learning
Credit Scoring and Its Applications
Credit Scoring and Its Applications
A Simple Approach to Ordinal Classification
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Multiple Reject Thresholds for Improving Classification Reliability
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Support Vector Machines with Embedded Reject Option
SVM '02 Proceedings of the First International Workshop on Pattern Recognition with Support Vector Machines
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Reducing the classification cost of support vector classifiers through an ROC-based reject rule
Pattern Analysis & Applications
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Improvement of reliability in banknote classification using reject option and local PCA
Information Sciences—Informatics and Computer Science: An International Journal
A ROC-based reject rule for dichotomizers
Pattern Recognition Letters
Optimizing abstaining classifiers using ROC analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Artificial Intelligence in Medicine
Learning to Classify Ordinal Data: The Data Replication Method
The Journal of Machine Learning Research
Reliability estimation of a statistical classifier
Pattern Recognition Letters
General solution and learning method for binary classification with performance constraints
Pattern Recognition Letters
Growing a multi-class classifier with a reject option
Pattern Recognition Letters
Classification with a Reject Option using a Hinge Loss
The Journal of Machine Learning Research
Artificial Intelligence in Medicine: 11th Conference on Artificial Intelligence in Medicine in Europe, AIME 2007, Amsterdam, The Netherlands, July 7-11, ... / Lecture Notes in Artificial Intelligence)
An Ordinal Data Method for the Classification with Reject Option
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Classification Methods with Reject Option Based on Convex Risk Minimization
The Journal of Machine Learning Research
An Optimum Class-Rejective Decision Rule and Its Evaluation
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
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
On optimum recognition error and reject tradeoff
IEEE Transactions on Information Theory
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Classification is one of the most important tasks of machine learning. Although the most well studied model is the two-class problem, in many scenarios there is the opportunity to label critical items for manual revision, instead of trying to automatically classify every item.In this paper we tailor a paradigm initially proposed for the classification of ordinal data to address the classification problem with reject option. The technique reduces the problem of classifying with reject option to the standard two-class problem. The introduced method is then mapped into support vector machines and neural networks. Finally, the framework is extended to multiclass ordinal data with reject option. An experimental study with synthetic and real datasets verifies the usefulness of the proposed approach.