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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #91723 An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data
sameAs	An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data
http://eprints.org/ontology/hasAccepted	An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data (PDF)
http://eprints.org/ontology/hasDocument	An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data (PDF) An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data (Other) An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data (Other) An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data (Other) An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data (Other) An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data (Other)
dc:hasVersion	An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data (PDF)
Title	An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
Date	2021
Creator	M.R.H. Maia A. Freitas A. Plastino
status	peer reviewed published
Publisher	IEEE
abstract	Coping with uncertainty is a very challenging issue in many real-world applications. However, conventional classification models usually assume there is no uncertainty in data at all. In order to fill this gap, there has been a growing number of studies addressing the problem of classification based on uncertain data. Although some methods resort to ignoring uncertainty or artificially removing it from data, it has been shown that predictive performance can be improved by actually incorporating information on uncertainty into classification models. This paper proposes an approach for building an ensemble of classifiers for uncertain categorical data based on biased random subspaces. Using Naive Bayes classifiers as base models, we have applied this approach to classify ageing-related genes based on real data, with uncertain features representing protein-protein interactions. Our experimental results show that models based on the proposed approach achieve better predictive performance than single Naive Bayes classifiers and conventional ensembles.
Is Part Of	https://kar.kent.ac.uk/id/repository
Subject	Q335 Artificial intelligence
list of authors	https://kar.kent.ac.uk/id/eprint/91723#authors
presented at	21st IEEE International Conference on Data Mining (ICDM 2021)
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #91723 An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data

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