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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 7 Issue 2, 2016.
Abstract: Bayesian network (BN) classifiers use different structures and different training parameters which leads to diversity in classification decisions. This work empirically shows that building an ensemble of several fine-tuned BN classifiers increases the overall classification accuracy. The accuracy of the constituent classifiers can be achieved by fine-tuning each classifier and the diversity is achieved using different BN classifiers. The proposed ensemble combines a Naive Bayes (NB) classifier, five different models of Tree Augmented Naive Bayes (TAN), and four different model of Bayesian Augmented Naive Bayes (BAN). This work also proposes a new Distance-based Diversity Measure (DDM) and uses it to analyze the diversity of the ensembles. The ensemble of fine-tuned classifier achieves better average classification accuracy than any of its constituent classifiers or the ensemble of un-tuned classifiers. Moreover, the empirical experiments present better significant results for many data sets.
Amel Alhussan and Khalil El Hindi, “An Ensemble of Fine-Tuned Heterogeneous Bayesian Classifiers” International Journal of Advanced Computer Science and Applications(IJACSA), 7(2), 2016. http://dx.doi.org/10.14569/IJACSA.2016.070259
@article{Alhussan2016,
title = {An Ensemble of Fine-Tuned Heterogeneous Bayesian Classifiers},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2016.070259},
url = {http://dx.doi.org/10.14569/IJACSA.2016.070259},
year = {2016},
publisher = {The Science and Information Organization},
volume = {7},
number = {2},
author = {Amel Alhussan and Khalil El Hindi}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.