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International Conference on Computer Aided Systems Theory

EUROCAST 2017: Computer Aided Systems Theory – EUROCAST 2017 pp 283–290Cite as

Measures for the Evaluation and Comparison of Graphical Model Structures

Part of the Lecture Notes in Computer Science book series (LNTCS,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

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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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Authors and Affiliations

  1. Heuristic and Evolutionary Algorithms Laboratory, University of Applied Sciences Upper Austria, Softwarepark 11, 4232, Hagenberg, Austria

    Gabriel Kronberger, Bogdan Burlacu, Michael Kommenda, Stephan Winkler & Michael Affenzeller

  2. Institute for Formal Models and Verification, Johannes Kepler University, Altenbergerstr. 69, 4040, Linz, Austria

    Bogdan Burlacu, Michael Kommenda & Michael Affenzeller

Authors
  1. Gabriel Kronberger
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  2. Bogdan Burlacu
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  3. Michael Kommenda
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  4. Stephan Winkler
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  5. Michael Affenzeller
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Corresponding author

Correspondence to Gabriel Kronberger .

Editor information

Editors and Affiliations

  1. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Roberto Moreno-Díaz

  2. Johannes Kepler University Linz, Linz, Austria

    Franz Pichler

  3. University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

    Alexis Quesada-Arencibia

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Kronberger, G., Burlacu, B., Kommenda, M., Winkler, S., Affenzeller, M. (2018). Measures for the Evaluation and Comparison of Graphical Model Structures. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_34

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  • DOI: https://doi.org/10.1007/978-3-319-74718-7_34

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