Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers
Ruz
G
A
author
Araya-Diaz
P
author
2018
English
Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed visualizing the probability relations amongst the attributes under this classification problem, a useful tool for decision-making for orthodontists.
WOS:000454097200001
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text
files/935_Ruz+Araya-Diaz2018.pdf
10.1155/2018/4075656
Ruz+Araya-Diaz2018
Complexity
Complexity
2018
Wiley-Hindawi
continuing
periodical
academic journal
4075656
14 pp
1076-2787