Robustness in sum-product networks with continuous and categorical data

Rob C. de Wit, Cassio P. de Campos, Diarmaid Conaty, Jesús Martínez del Rincon

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the Eleventh International Symposium on Imprecise Probabilities: Theories and Applications
EditorsJasper De Bock, Cassio P. de Campos, Gert de Cooman, Erik Quaeghebeur, Gregory Wheeler
PublisherPMLR
Pages156-158
Number of pages3
Publication statusPublished - 2019
Event11th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2019 - Ghent, Belgium
Duration: 3 Jul 20196 Jul 2019

Publication series

NameProceedings of Machine Learning Research
PublisherPMLR
Volume103

Conference

Conference11th International Symposium on Imprecise Probability: Theories and Applications, ISIPTA 2019
Abbreviated titleISIPTA 2019
CountryBelgium
CityGhent
Period3/07/196/07/19

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