文章基本信息

标题：An Ensemble of Fine-Tuned Heterogeneous Bayesian Classifiers
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作者：Amel Alhussan ; Khalil El Hindi
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2016
卷号：7
期号：2
DOI：10.14569/IJACSA.2016.070259
出版社：Science and Information Society (SAI)
摘要：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.
关键词：thesai; IJACSA; thesai.org; journal; IJACSA papers; Ensemble classifier; Bayesian Network (BN) classifiers; Fine-tuned BN classifiers; Stacking; Diversity