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MCS
2005
Springer

Ensemble Confidence Estimates Posterior Probability

4 years 5 months ago
Ensemble Confidence Estimates Posterior Probability
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.
Michael Muhlbaier, Apostolos Topalis, Robi Polikar
Added 28 Jun 2010
Updated 28 Jun 2010
Type Conference
Year 2005
Where MCS
Authors Michael Muhlbaier, Apostolos Topalis, Robi Polikar
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