TY - JOUR
T1 - Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers
AU - Ruz, Gonzalo A.
AU - Araya-Díaz, Pamela
N1 - 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
SN - 1076-2787
VL - 2018
JO - Complexity
JF - Complexity
M1 - 4075656
ER -