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