Jul 11, 2020 · We focus on multi-dimensional Bayesian network classifiers, which directly cope with multi-dimensional classification and consider dependencies among class ...
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These clas- sifiers include one or more class variables and multiple feature variables, which need not be modelled as being dependent on every class variable.
A comprehensive survey of this state-of-the-art classification model is offered by covering aspects related to their learning and inference process ...
In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers. We focus on multi- ...
We introduce the family of multi-dimensional Bayesian network classiflers. These clas- siflers include one or more class variables and multiple feature ...
This paper tackles the partial label ranking problem by transforming the ranking with ties into a set of discrete variables representing the preference ...
Mar 20, 2024 · In this article, we review and expand the set of performance evaluation measures suitable for assessing multi-dimensional classifiers. We focus ...
Multi-dimensional Bayesian network classifiers: A survey · List of references · Publications that cite this publication.
Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models tailored to solving multi-dimensional clas- sification problems, where ...
Multi-dimensional classification aims at finding a function that assigns a vector of class values to a given vector of features.
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