The nature of statistical learning theory
The nature of statistical learning theory
Classification by pairwise coupling
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Experiments on Solving Multiclass Learning Problems by n2-classifier
ECML '98 Proceedings of the 10th European Conference on Machine Learning
On the Decomposition of Polychotomies into Dichotomies
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
A Probabilistic Classification System for Predicting the Cellular Localization Sites of Proteins
Proceedings of the Fourth International Conference on Intelligent Systems for Molecular Biology
Attribute Selection with a Multi-objective Genetic Algorithm
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Confidence Evaluation for Combining Diverse Classifiers
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
The Journal of Machine Learning Research
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Bias-Variance Analysis of Support Vector Machines for the Development of SVM-Based Ensemble Methods
The Journal of Machine Learning Research
Using diversity measures for generating error-correcting output codes in classifier ensembles
Pattern Recognition Letters
A Multi-Expert System to Classify Fluorescent Intensity in Antinuclear Autoantibodies Testing
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
A Hybrid Multi-Expert Systems for HEp-2 Staining Pattern Classification
ICIAP '07 Proceedings of the 14th International Conference on Image Analysis and Processing
Pattern Analysis & Applications
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
On rejecting unreliably classified patterns
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
To reject or not to reject: that is the question-an answer in caseof neural classifiers
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A neural classifier enabling high-throughput topological analysis of lymphocytes in tissue sections
IEEE Transactions on Information Technology in Biomedicine
Mining knowledge for HEp-2 cell image classification
Artificial Intelligence in Medicine
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Polichotomies on imbalanced domains by one-per-class compensated reconstruction rule
SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
A random forest classifier for lymph diseases
Computer Methods and Programs in Biomedicine
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Typical pattern recognition applications require to handle both binary and multiclass classification problems. Several researchers have pointed out that obtaining a classifier that discriminates between two classes is much easier than building one that simultaneously distinguishes among all classes. This observation has motivated substantial research on using a pool of binary classifiers to address multiclass problems. Such an approach is also named as decomposition method. Anyway, the performance of a given classification system can be sometimes unsatisfactory for the needs of real applications, especially when these are characterized by large data variability and/or significant amount of noise. In these cases it is important that the classification system is able to estimate the reliability of its decision for each sample under test. This estimate could be used, for example, for deciding to reject a sample instead of running the risk of misclassifying it, so improving the overall system performance. Based on these motivations, this paper defines a reliability estimator for decomposition schemes belonging to the One-per-Class framework. The estimator is based on the reliabilities provided by each binary classifier, on the status of their outputs while it is independent of their design. The performance of the proposed approach has been assessed on private and public medical datasets, showing that it can be used to improve the classification performance of the One-per-Class scheme with respect to both multiclass classifiers and other well-known decomposition schemes.