Learning possibilistic networks from data: a survey Article Swipe
Maroua Haddad
,
Philippe Leray
,
Nahla Ben Amor
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.2991/ifsa-eusflat-15.2015.30
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.2991/ifsa-eusflat-15.2015.30
Possibilistic networks are important tools for modelling and reasoning, especially in the presence of imprecise and/or uncertain information.These graphical models have been successfully used in several real applications.Since their construction by experts is complex and time consuming, several researchers have tried to learn them from data.In this paper, we try to present and discuss relevant state-of-the-art works related to learning possibilistic networks structure from data.In fact, we give an overview of methods that have already been proposed in this context and limitations of each one of them towards recent researches developed in possibility theory framework.We also present two learning possibilistic networks parameters methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.2991/ifsa-eusflat-15.2015.30
- https://download.atlantis-press.com/article/23541.pdf
- OA Status
- hybrid
- Cited By
- 5
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W1904962112
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W1904962112Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.2991/ifsa-eusflat-15.2015.30Digital Object Identifier
- Title
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Learning possibilistic networks from data: a surveyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2015Year of publication
- Publication date
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2015-01-01Full publication date if available
- Authors
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Maroua Haddad, Philippe Leray, Nahla Ben AmorList of authors in order
- Landing page
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https://doi.org/10.2991/ifsa-eusflat-15.2015.30Publisher landing page
- PDF URL
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https://download.atlantis-press.com/article/23541.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://download.atlantis-press.com/article/23541.pdfDirect OA link when available
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Computer science, Context (archaeology), Artificial intelligence, Graphical model, Data science, Machine learning, State (computer science), Algorithm, Paleontology, BiologyTop concepts (fields/topics) attached by OpenAlex
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5Total citation count in OpenAlex
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2020: 1, 2019: 1, 2017: 2, 2016: 1Per-year citation counts (last 5 years)
- References (count)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| institutions_distinct_count | 3 |
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| citation_normalized_percentile.is_in_top_10_percent | False |