Robustness in Sum-Product Networks with continuous and categorical data

Rob de Wit, Cassio P. de Campos, Diarmaid Conaty, Jesus Martinez del Rincon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 publicationInternational symposium on imprecise probabilities
Publication statusPublished - 27 Jul 2019
Event International symposium on imprecise probabilities - Ghent, Belgium
Duration: 03 Jul 201906 Jul 2019
Conference number: 11th


Conference International symposium on imprecise probabilities
Abbreviated titleISIPTA
Internet address


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