Building knowledge scouts using KGL metalanguage
Fundamenta Informaticae
Machine Learning
Transductive Confidence Machines for Pattern Recognition
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Is Combining Classifiers Better than Selecting the Best One
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Characterizing Model Erros and Differences
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
WMP '00 Proceedings of the Workshop on Multiset Processing: Multiset Processing, Mathematical, Computer Science, and Molecular Computing Points of View
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
C4.5 competence map: a phase transition-inspired approach
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Algorithmic Learning in a Random World
Algorithmic Learning in a Random World
Optimizing abstaining classifiers using ROC analysis
ICML '05 Proceedings of the 22nd international conference on Machine learning
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Case-Based Approximate Reasoning (Theory and Decision Library B)
Case-Based Approximate Reasoning (Theory and Decision Library B)
Theoretical and Experimental Study of a Meta-Typicalness Approach for Reliable Classification
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A Tutorial on Conformal Prediction
The Journal of Machine Learning Research
The ROC isometrics approach to construct reliable classifiers
Intelligent Data Analysis
Version Space Support Vector Machines
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Transduction with confidence and credibility
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Artificial Intelligence in Medicine
Single-stacking conformity approach to reliable classification
AIMSA'10 Proceedings of the 14th international conference on Artificial intelligence: methodology, systems, and applications
Ensemble of binary learners for reliable text categorization with a reject option
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
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The conformity framework has recently been proposed for the task of reliable classification. Given a classifier B, the framework allows to obtain p-values of the classifications assigned to individual instances. However, applying the framework is a difficult problem: we need to construct an instance non-conformity function for the classifier B. To avoid constructing such a function we propose a meta-conformity approach. If a conformity-based classifier M is available, the approach is to train M as a meta classifier that predicts the correctness of each classification of the classifier B. In this way the classification p-values of the classifier B are represented by the classification p-values of the classifier M. The meta-conformity approach can be used for constructing classifiers with predefined generalization performance. Experiments show that the approach results in classifiers that can outperform existing conformity-based classifiers.