Structural learning of probabilistic graphical models of cumulative phenomena

Daniele Ramazzotti, Marco S. Nobile, Marco Antoniotti, Alex Graudenzi

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

2 Citations (Scopus)

Abstract

One of the critical issues when adopting Bayesian networks (BNs) to model dependencies among random variables is to “learn” their structure. This is a well-known NP-hard problem in its most general and classical formulation, which is furthermore complicated by known pitfalls such as the issue of I-equivalence among different structures. In this work we restrict the investigation to a specific class of networks, i.e., those representing the dynamics of phenomena characterized by the monotonic accumulation of events. Such phenomena allow to set specific structural constraints based on Suppes’ theory of probabilistic causation and, accordingly, to define constrained BNs, named Suppes-Bayes Causal Networks (SBCNs). Within this framework, we study the structure learning of SBCNs via extensive simulations with various state-of-the-art search strategies, such as canonical local search techniques and Genetic Algorithms. This investigation is intended to be an extension and an in-depth clarification of our previous works on SBCN structure learning. Among the main results, we show that Suppes’ constraints do simplify the learning task, by reducing the solution search space and providing a temporal ordering on the variables, which simplifies the complications derived by I-equivalent structures. Finally, we report on tradeoffs among different optimization techniques that can be used to learn SBCNs.

Original languageEnglish
Title of host publicationComputational Science – ICCS 2018 - 18th International Conference, Proceedings
EditorsHaohuan Fu, Valeria V. Krzhizhanovskaya, Michael Harold Lees, Peter M. Sloot, Jack Dongarra, Yong Shi, Yingjie Tian
Place of PublicationCham
PublisherSpringer
Pages678-693
Number of pages16
ISBN (Electronic)978-3-319-93698-7
ISBN (Print)978-3-319-93697-0
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes
Event18th International Conference on Computational Science, ICCS 2018 - Wuxi, China
Duration: 11 Jun 201813 Jun 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10860 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference18th International Conference on Computational Science, ICCS 2018
CountryChina
CityWuxi
Period11/06/1813/06/18

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  • Cite this

    Ramazzotti, D., Nobile, M. S., Antoniotti, M., & Graudenzi, A. (2018). Structural learning of probabilistic graphical models of cumulative phenomena. In H. Fu, V. V. Krzhizhanovskaya, M. H. Lees, P. M. Sloot, J. Dongarra, Y. Shi, & Y. Tian (Eds.), Computational Science – ICCS 2018 - 18th International Conference, Proceedings (pp. 678-693). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10860 LNCS). Cham: Springer. https://doi.org/10.1007/978-3-319-93698-7_52