An Ensemble of Fine-Tuned Heterogeneous Bayesian Classifiers

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}
}