Classification based on possibilistic likelihood

Serrurier, M; Prade, H

Classification based on possibilistic likelihood

Serrurier, M Prade, H

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Publication Type:: Conference Proceeding
Citation:: Communications in Computer and Information Science, 2012, 299 CCIS (PART 3), pp. 420 - 429
Issue Date:: 2012-11-05

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Field	Value	Language
dc.contributor.author	Serrurier, M	en_US
dc.contributor.author	Prade, H https://orcid.org/0000-0003-4586-8527	en_US
dc.date.issued	2012-11-05	en_US
dc.identifier.citation	Communications in Computer and Information Science, 2012, 299 CCIS (PART 3), pp. 420 - 429	en_US
dc.identifier.isbn	9783642317170	en_US
dc.identifier.issn	1865-0929	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32835
dc.description.abstract	Classification models usually associate one class for each new instance. This kind of prediction doesn't reflect the uncertainty that is inherent in any machine learning algorithm. Probabilistic approaches rather focus on computing a probability distribution over the classes. However, making such a computation may be tricky and requires a large amount of data. In this paper, we propose a method based on the notion of possibilistic likelihood in order to learn a model that associates a possibility distribution over the classes to a new instance. Possibility distributions are here viewed as an upper bound of a family of probability distributions. This allows us to capture the epistemic uncertainty associated with the model in a faithful way. The model is based on a set of kernel functions and is obtained through an optimization process performed by a particle swarm algorithm. We experiment our method on benchmark dataset and compares it with a naive Bayes classifier. © 2012 Springer-Verlag Berlin Heidelberg.	en_US
dc.relation.ispartof	Communications in Computer and Information Science	en_US
dc.relation.isbasedon	10.1007/978-3-642-31718-7_44	en_US
dc.title	Classification based on possibilistic likelihood	en_US
dc.type	Conference Proceeding
utslib.citation.volume	PART 3	en_US
utslib.citation.volume	299 CCIS	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	PART 3	en_US
pubs.publication-status	Published	en_US
pubs.volume	299 CCIS	en_US

Abstract:

Classification models usually associate one class for each new instance. This kind of prediction doesn't reflect the uncertainty that is inherent in any machine learning algorithm. Probabilistic approaches rather focus on computing a probability distribution over the classes. However, making such a computation may be tricky and requires a large amount of data. In this paper, we propose a method based on the notion of possibilistic likelihood in order to learn a model that associates a possibility distribution over the classes to a new instance. Possibility distributions are here viewed as an upper bound of a family of probability distributions. This allows us to capture the epistemic uncertainty associated with the model in a faithful way. The model is based on a set of kernel functions and is obtained through an optimization process performed by a particle swarm algorithm. We experiment our method on benchmark dataset and compares it with a naive Bayes classifier. © 2012 Springer-Verlag Berlin Heidelberg.

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http://hdl.handle.net/10453/32835