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Measures for the Evaluation and Comparison of Graphical Model Structures

  • Gabriel KronbergerEmail author
  • Bogdan Burlacu
  • Michael Kommenda
  • Stephan Winkler
  • Michael Affenzeller
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10671)

Abstract

Structure learning is the identification of the structure of graphical models based solely on observational data and is NP-hard. An important component of many structure learning algorithms are heuristics or bounds to reduce the size of the search space. We argue that variable relevance rankings that can be easily calculated for many standard regression models can be used to improve the efficiency of structure learning algorithms. In this contribution, we describe measures that can be used to evaluate the quality of variable relevance rankings, especially the well-known normalized discounted cumulative gain (NDCG). We evaluate and compare different regression methods using the proposed measures and a set of linear and non-linear benchmark problems.

Keywords

Graphical models Structure learning Regression 

Notes

Acknowledgements

The authors gratefully acknowledge financial support by the Austrian Research Promotion Agency (FFG) and the Government of Upper Austria within the COMET Project #843532 Heuristic Optimization in Production and Logistics (HOPL).

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Heuristic and Evolutionary Algorithms LaboratoryUniversity of Applied Sciences Upper AustriaHagenbergAustria
  2. 2.Institute for Formal Models and VerificationJohannes Kepler UniversityLinzAustria

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