TY - JOUR

T1 - Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

AU - Ruz, Gonzalo A.

AU - Araya-Díaz, Pamela

N1 - Funding Information:
The authors would like to thank Conicyt-Chile under grant Fondecyt 1180706 and Basal (CONICYT)-CMM, for financially supporting this research.
Publisher Copyright:
© 2018 Gonzalo A. Ruz and Pamela Araya-Díaz.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85058934363&partnerID=8YFLogxK

U2 - 10.1155/2018/4075656

DO - 10.1155/2018/4075656

M3 - Article

AN - SCOPUS:85058934363

VL - 2018

JO - Complexity

JF - Complexity

SN - 1076-2787

M1 - 4075656

ER -