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An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data

Maia, M.R.H., Plastino, A., Freitas, Alex A. (2021) An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data. In: Proceedings: 21st IEEE International Conference on Data Mining (ICDM 2021). . pp. 1216-1221. IEEE, Los Alamitos, CA, USA ISBN 978-1-66542-398-4. (doi:10.1109/ICDM51629.2021.00148) (KAR id:91723)


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.

Item Type: Conference or workshop item (Proceeding)
DOI/Identification number: 10.1109/ICDM51629.2021.00148
Uncontrolled keywords: machine learning, data mining, classification, bioinformatics
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Alex Freitas
Date Deposited: 25 Nov 2021 09:26 UTC
Last Modified: 10 Feb 2022 11:53 UTC
Resource URI: (The current URI for this page, for reference purposes)

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