Same-Decision Probability: Threshold Robustness and Application to Explanation

Silja Renooij
; Proceedings of the Ninth International Conference on Probabilistic Graphical Models, PMLR 72:368-379, 2018.

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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v72-renooij18a, title = {Same-Decision Probability: Threshold Robustness and Application to Explanation}, author = {Renooij, Silja}, booktitle = {Proceedings of the Ninth International Conference on Probabilistic Graphical Models}, pages = {368--379}, year = {2018}, editor = {Václav Kratochvíl and Milan Studený}, volume = {72}, series = {Proceedings of Machine Learning Research}, address = {Prague, Czech Republic}, month = {11--14 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v72/renooij18a/renooij18a.pdf}, url = {http://proceedings.mlr.press/v72/renooij18a.html}, 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.} }
Endnote
%0 Conference Paper %T Same-Decision Probability: Threshold Robustness and Application to Explanation %A Silja Renooij %B Proceedings of the Ninth International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2018 %E Václav Kratochvíl %E Milan Studený %F pmlr-v72-renooij18a %I PMLR %J Proceedings of Machine Learning Research %P 368--379 %U http://proceedings.mlr.press %V 72 %W PMLR %X 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.
APA
Renooij, S.. (2018). Same-Decision Probability: Threshold Robustness and Application to Explanation. Proceedings of the Ninth International Conference on Probabilistic Graphical Models, in PMLR 72:368-379

Related Material