Abstract
The same-decision probability (SDP) is a confidence measure for threshold-based decisions. In this paper we detail various properties of the SDP that allow for studying its robustness to changes in the threshold value upon which a decision is based. In addition to expressing confidence in a decision, the SDP has proven to be a useful tool in other contexts, such as that of information gathering. We demonstrate that the properties of the SDP as established in this paper allow for its application in the context of explaining Bayesian network classifiers as well.
Original language | English |
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Title of host publication | Proceedings of the Ninth International Conference on Probabilistic Graphical Models (PGM) |
Editors | Václav Kratochvíl, Milan Studený |
Pages | 368-379 |
Publication status | Published - Sept 2018 |
Event | Ninth International Conference on Probabilistic Graphical Models (PGM) - Prague, Czech Republic Duration: 11 Sept 2018 → 14 Sept 2018 Conference number: 9 |
Publication series
Name | Proceedings of Machine Learning Research |
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Publisher | PMLR |
Volume | 72 |
Conference
Conference | Ninth International Conference on Probabilistic Graphical Models (PGM) |
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Abbreviated title | PGM |
Country/Territory | Czech Republic |
City | Prague |
Period | 11/09/18 → 14/09/18 |
Keywords
- Bayesian network classifiers
- threshold-based decisions
- Same-decision probability
- robustness
- explanations