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 Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classifier Conditional Posterior Probabilities
SSPR '98/SPR '98 Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
An ensemble approach for data fusion with learn++
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Local linear perceptrons for classification
IEEE Transactions on Neural Networks
Using weighted dynamic classifier selection methods in ensembles with different levels of diversity
International Journal of Hybrid Intelligent Systems - Hybrid Intelligent systems in Ensembles
IEEE Transactions on Neural Networks
Using weighted combination-based methods in ensembles with different levels of diversity
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Subpopulation-specific confidence designation for more informative biomedical classification
Artificial Intelligence in Medicine
Random subspace support vector machine ensemble for reliable face recognition
International Journal of Biometrics
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We have previously introduced the Learn++ algorithm that provides surprisingly promising performance for incremental learning as well as data fusion applications. In this contribution we show that the algorithm can also be used to estimate the posterior probability, or the confidence of its decision on each test instance. On three increasingly difficult tests that are specifically designed to compare posterior probability estimates of the algorithm to that of the optimal Bayes classifier, we have observed that estimated posterior probability approaches to that of the Bayes classifier as the number of classifiers in the ensemble increase. This satisfying and intuitively expected outcome shows that ensemble systems can also be used to estimate confidence of their output.