Fine tuning the tree augmented Naïve Bayes (FTTAN) learning algorithm

In this work, we adapt the fine tuning algorithm of Naïve Bayes (NB) for Tree Augmented Naïve Bayes (TAN). The adapted algorithm, takes into consideration the differences in structure between NB and TAN. The algorithm augments the regular TAN learning phase with a fine tuning phase in which the pr...

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Bibliographic Details
Published in:2015 SAI Intelligent Systems Conference (IntelliSys) pp. 72 - 79
Main Authors: Alhussan, Amel, El Hindi, Khalil
Format: Conference Proceeding
Language:English
Published: IEEE 01.11.2015
Subjects:
Bayesian Network (BN)
Classification accuracy
Classification algorithms
Estimation
fine tuning
Intelligent systems
Niobium
Prediction algorithms
TAN models
Training
Tree Augmented Naïve Bayes (TAN)
Tuning
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Summary:In this work, we adapt the fine tuning algorithm of Naïve Bayes (NB) for Tree Augmented Naïve Bayes (TAN). The adapted algorithm, takes into consideration the differences in structure between NB and TAN. The algorithm augments the regular TAN learning phase with a fine tuning phase in which the probability terms are fine tuned to give better classification accuracy. The fine tuning algorithm is applied on five models of TAN: TAN search, K2 search, tabu search, Hill Climber search, and Repeated Hill Climber search. Our empirical results show that fine tuning TAN significantly improves the average classification accuracy of all TAN models in many domains.
DOI:10.1109/IntelliSys.2015.7361087