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Borchani, Hanen and Bielza Lozoya, Maria Concepcion and Larrañaga Múgica, Pedro and Martínez-Martín, Pablo (2012). Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39). "Journal of Biomedical Informatics", v. 45 (n. null); pp. 1175-1184. ISSN 1532-0464.
|Title:||Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39)|
|Título de Revista/Publicación:||Journal of Biomedical Informatics|
|Freetext Keywords:||Multi-dimensional Bayesian network classifiers, Markov blanket, Multi-dimensional classification, Parkinson’s disease, EQ-5D, PDQ-39, Red de clasificación multidimensional Bayesian, Markov, Clasificación multidimensional, Enfermedad de Parkinson.|
|Faculty:||Facultad de Informática (UPM)|
|Creative Commons Licenses:||Recognition - No derivative works - Non commercial|
Official URL: http://www.elsevier.com/journals/title/a
Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models recently proposed to deal with multi-dimensional classification problems, where each instance in the data set has to be assigned to more than one class variable. In this paper, we propose a Markov blanket-based approach for learning MBCs from data. Basically, it consists of determining the Markov blanket around each class variable using the HITON algorithm, then specifying the directionality over the MBC subgraphs. Our approach is applied to the prediction problem of the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39) in order to estimate the health-related quality of life of Parkinson’s patients. Fivefold cross-validation experiments were carried out on randomly generated synthetic data sets, Yeast data set, as well as on a real-world Parkinson’s disease data set containing 488 patients. The experimental study, including comparison with additional Bayesian network-based approaches, back propagation for multi-label learning, multi-label k-nearest neighbor, multinomial logistic regression, ordinary least squares, and censored least absolute deviations, shows encouraging results in terms of predictive accuracy as well as the identification of dependence relationships among class and feature variables.
|Deposited by:||Memoria Investigacion|
|Deposited on:||13 Jun 2013 17:26|
|Last Modified:||21 Apr 2016 15:23|