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Multi‑dimensional Bayesian network classifiers: a survey
- Source :
- Artificial Intelligence Review, ISSN 0269-2821, 2021-01, Vol. 54, No. 1
- Publication Year :
- 2021
-
Abstract
- Multi-dimensional classification is a cutting-edge problem, in which the values of multiple class variables have to be simultaneously assigned to a given example. It is an extension of the well known multi-label subproblem, in which the class variables are all binary. In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers. We focus on multi-dimensional Bayesian network classifiers, which directly cope with multi-dimensional classification and consider dependencies among class variables. A comprehensive survey of this state-of-the-art classification model is offered by covering aspects related to their learning and inference process complexities. We also describe algorithms for structural learning, provide real-world applications where they have been used, and compile a collection of related software.
Details
- Database :
- OAIster
- Journal :
- Artificial Intelligence Review, ISSN 0269-2821, 2021-01, Vol. 54, No. 1
- Notes :
- application/pdf, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1378452076
- Document Type :
- Electronic Resource