Robustness in Sum-Product Networks with Continuous and Categorical Data

Rob de Wit, Cassio P. de Campos, Diarmaid Conaty, Jesus Martinez del Rincon
Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, PMLR 103:156-158, 2019.

Abstract

Sum-product networks are a popular family of probabilistic graphical models for which marginal inference can be performed in polynomial time. After learning sum-product networks from scarce data, small variations of parameters could lead to different conclusions. We adapt the robustness measure created for categorical credal sum-product networks to domains with both continuous and categorical variables. We apply this approach to a real-world dataset of online purchases where the goal is to identify fraudulent cases. We empirically show that such credal models can better discriminate between easy and hard instances than simply using the probability of the most probable class.

Cite this Paper

BibTeX
@InProceedings{pmlr-v103-de-wit19a, title = {Robustness in Sum-Product Networks with Continuous and Categorical Data}, author = {{de Wit}, Rob and {de Campos}, Cassio P. and Conaty, Diarmaid and {del Rincon}, Jesus Martinez}, booktitle = {Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications}, pages = {156--158}, year = {2019}, editor = {De Bock, Jasper and de Campos, Cassio P. and de Cooman, Gert and Quaeghebeur, Erik and Wheeler, Gregory}, volume = {103}, series = {Proceedings of Machine Learning Research}, month = {03--06 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v103/de-wit19a/de-wit19a.pdf}, url = {https://proceedings.mlr.press/v103/de-wit19a.html}, abstract = {Sum-product networks are a popular family of probabilistic graphical models for which marginal inference can be performed in polynomial time. After learning sum-product networks from scarce data, small variations of parameters could lead to different conclusions. We adapt the robustness measure created for categorical credal sum-product networks to domains with both continuous and categorical variables. We apply this approach to a real-world dataset of online purchases where the goal is to identify fraudulent cases. We empirically show that such credal models can better discriminate between easy and hard instances than simply using the probability of the most probable class.} }
Endnote
%0 Conference Paper %T Robustness in Sum-Product Networks with Continuous and Categorical Data %A Rob de Wit %A Cassio P. de Campos %A Diarmaid Conaty %A Jesus Martinez del Rincon %B Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications %C Proceedings of Machine Learning Research %D 2019 %E Jasper De Bock %E Cassio P. de Campos %E Gert de Cooman %E Erik Quaeghebeur %E Gregory Wheeler %F pmlr-v103-de-wit19a %I PMLR %P 156--158 %U https://proceedings.mlr.press/v103/de-wit19a.html %V 103 %X Sum-product networks are a popular family of probabilistic graphical models for which marginal inference can be performed in polynomial time. After learning sum-product networks from scarce data, small variations of parameters could lead to different conclusions. We adapt the robustness measure created for categorical credal sum-product networks to domains with both continuous and categorical variables. We apply this approach to a real-world dataset of online purchases where the goal is to identify fraudulent cases. We empirically show that such credal models can better discriminate between easy and hard instances than simply using the probability of the most probable class.
APA
de Wit, R., de Campos, C.P., Conaty, D. & del Rincon, J.M.. (2019). Robustness in Sum-Product Networks with Continuous and Categorical Data. Proceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications, in Proceedings of Machine Learning Research 103:156-158 Available from https://proceedings.mlr.press/v103/de-wit19a.html.

Related Material