Measures for the Evaluation and Comparison of Graphical Model Structures

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@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