Trick, Susanne ; Rothkopf, Constantin A. (2022)
Bayesian Classifier Fusion with an Explicit Model of Correlation.
The 25th International Conference on Artificial Intelligence. Valencia, Spain (30.03.2022-01.04.2022)
Conference or Workshop Item, Bibliographie
Abstract
Combining the outputs of multiple classifiers or experts into a single probabilistic classification is a fundamental task in machine learning with broad applications from classifier fusion to expert opinion pooling. Here we present a hierarchical Bayesian model of probabilistic classifier fusion based on a new correlated Dirichlet distribution. This distribution explicitly models positive correlations between marginally Dirichlet-distributed random vectors thereby allowing explicit modeling of correlations between base classifiers or experts. The proposed model naturally accommodates the classic Independent Opinion Pool and other independent fusion algorithms as special cases. It is evaluated by uncertainty reduction and correctness of fusion on synthetic and real-world data sets. We show that a change in performance of the fused classifier due to uncertainty reduction can be Bayes optimal even for highly correlated base classifiers.
| Item Type: | Conference or Workshop Item |
|---|---|
| Erschienen: | 2022 |
| Creators: | Trick, Susanne ; Rothkopf, Constantin A. |
| Type of entry: | Bibliographie |
| Title: | Bayesian Classifier Fusion with an Explicit Model of Correlation |
| Language: | English |
| Date: | 28 September 2022 |
| Place of Publication: | Valencia, Spain |
| Publisher: | PMLR |
| Book Title: | Proceedings of The 25th International Conference on Artificial Intelligence and Statistics |
| Series: | Proceedings of Machine Learning Research |
| Series Volume: | 151 |
| Event Title: | The 25th International Conference on Artificial Intelligence |
| Event Location: | Valencia, Spain |
| Event Dates: | 30.03.2022-01.04.2022 |
| Corresponding Links: | |
| Abstract: | Combining the outputs of multiple classifiers or experts into a single probabilistic classification is a fundamental task in machine learning with broad applications from classifier fusion to expert opinion pooling. Here we present a hierarchical Bayesian model of probabilistic classifier fusion based on a new correlated Dirichlet distribution. This distribution explicitly models positive correlations between marginally Dirichlet-distributed random vectors thereby allowing explicit modeling of correlations between base classifiers or experts. The proposed model naturally accommodates the classic Independent Opinion Pool and other independent fusion algorithms as special cases. It is evaluated by uncertainty reduction and correctness of fusion on synthetic and real-world data sets. We show that a change in performance of the fused classifier due to uncertainty reduction can be Bayes optimal even for highly correlated base classifiers. |
| Divisions: | 03 Department of Human Sciences 03 Department of Human Sciences > Institute for Psychology 03 Department of Human Sciences > Institute for Psychology > Psychology of Information Processing Zentrale Einrichtungen Zentrale Einrichtungen > Centre for Cognitive Science (CCS) |
| Date Deposited: | 28 Sep 2022 13:06 |
| Last Modified: | 28 Sep 2022 13:06 |
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