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Entropy 2015, 17(7), 5085-5100; doi:10.3390/e17075085

Averaged Extended Tree Augmented Naive Classifier

EEECS, Queen's University Belfast, University Road, Belfast BT7 1NN, UK
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Author to whom correspondence should be addressed.
Academic Editors: Carlos Alberto De Bragança Pereira and Adriano Polpo
Received: 8 June 2015 / Revised: 10 June 2015 / Accepted: 17 June 2015 / Published: 21 July 2015
(This article belongs to the Special Issue Inductive Statistical Methods)
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Abstract

This work presents a new general purpose classifier named Averaged Extended Tree Augmented Naive Bayes (AETAN), which is based on combining the advantageous characteristics of Extended Tree Augmented Naive Bayes (ETAN) and Averaged One-Dependence Estimator (AODE) classifiers. We describe the main properties of the approach and algorithms for learning it, along with an analysis of its computational time complexity. Empirical results with numerous data sets indicate that the new approach is superior to ETAN and AODE in terms of both zero-one classification accuracy and log loss. It also compares favourably against weighted AODE and hidden Naive Bayes. The learning phase of the new approach is slower than that of its competitors, while the time complexity for the testing phase is similar. Such characteristics suggest that the new classifier is ideal in scenarios where online learning is not required. View Full-Text
Keywords: classification; tree augmented Naive Bayes; model averaging classification; tree augmented Naive Bayes; model averaging
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Meehan, A.; de Campos, C.P. Averaged Extended Tree Augmented Naive Classifier. Entropy 2015, 17, 5085-5100.

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