An Ensemble of Naive Bayes Classifiers for Uncertain Categorical Data

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.