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A Hybrid Approach for Probabilistic Relational Models Structure Learning

  • Mouna Ben IshakEmail author
  • Philippe Leray
  • Nahla Ben Amor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9897)

Abstract

Probabilistic relational models (PRMs) extend Bayesian networks (BNs) to a relational data mining context. Just like BNs, the structure and parameters of a PRM must be either set by an expert or learned from data. Learning the structure remains the most complicated issue as it is a NP-hard problem. Existing approaches for PRM structure learning are inspired from classical methods of learning the BN structure. Extensions for the constraint-based and score-based methods have been proposed. However, hybrid methods are not yet adapted to relational domains, although some of them show better experimental performance, in the classical context, than constraint-based and score-based methods, such as the Max-Min Hill Climbing (MMHC) algorithm. In this paper, we present an adaptation of this latter to relational domains and we made an empirical evaluation of our algorithm. We provide an experimental study where we compare our new approach to the state-of-the art relational structure learning algorithms.

Keywords

Probabilistic relational model Relational structure learning Relational Max-Min Hill Climbing 

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mouna Ben Ishak
    • 1
    Email author
  • Philippe Leray
    • 2
  • Nahla Ben Amor
    • 1
  1. 1.LARODEC Laboratory, ISGUniversité de TunisTunisTunisia
  2. 2.DUKe Research Group, LINA Laboratory UMR 6241University of NantesNantesFrance

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