Measures for the Evaluation and Comparison of Graphical Model Structures

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

@InProceedings{6345,
  author =       "Gabriel K. Kronberger and Bogdan Burlacu and 
                 Michael Kommenda and Stephan Winkler and Michael Affenzeller",
  title =        "Measures for the Evaluation and Comparison of
                 Graphical Model Structures",
  booktitle =    "Computer Aided Systems Theory, EUROCAST 2017",
  year =         "2017",
  editor =       "Roberto Moreno-Diaz and Franz Pichler and 
                 Alexis Quesada-Arencibia",
  volume =       "10671",
  series =       "Lecture Notes in Computer Science",
  pages =        "283--290",
  address =      "Las Palmas de Gran Canaria, Spain",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming, Graphical
                 models, Structure learning, Regression",
  isbn13 =       "978-3-319-74718-7",
  URL =          "https://link.springer.com/chapter/10.1007/978-3-319-74718-7_34",
  DOI =          "doi:10.1007/978-3-319-74718-7_34",
  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.",
  notes =        "Published 2018?",
}

Genetic Programming entries for Gabriel Kronberger Bogdan Burlacu Michael Kommenda Stephan M Winkler Michael Affenzeller

Citations