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Author (up) Ruz, G.A.; Araya-Diaz, P. pdf  doi
  Title Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers Type Journal Article
  Year 2018 Publication Complexity Abbreviated Journal Complexity  
  Volume 4075656 Issue Pages 14 pp  
  Abstract 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.  
  Address [Ruz, Gonzalo A.] Univ Adolfo Ibanez, Fac Ingn & Ciencias, Ave Diagonal Torres 2640, Santiago, Chile, Email:  
  Corporate Author Thesis  
  Publisher Wiley-Hindawi Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1076-2787 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000454097200001 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 954  
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