Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

Signal propagation in Bayesian networks and its relationship with intrinsically multivariate predictive variables

Autor Martins, David C. Google Scholar
Oliveira, Evaldo A. de Autor UNIFESP Google Scholar
Braga-Neto, Ulisses M. Google Scholar
Hashimoto, Ronaldo F. Google Scholar
Cesar, Roberto M. Google Scholar
Instituição Fed Univ ABC
Universidade Federal de São Paulo (UNIFESP)
Universidade de São Paulo (USP)
Brazilian Bioethanol Sci & Technol Lab
Texas A&M Univ
Resumo A set of predictor variables is said to be intrinsically multivariate predictive (IMP) for a target variable if all properly contained subsets of the predictor set are poor predictors of the. target but the full set predicts the target with great accuracy. in a previous article, the main properties of IMP Boolean variables have been analytically described, including the introduction of the IMP score, a metric based on the coefficient of determination (CoD) as a measure of predictiveness with respect to the target variable. It was shown that the IMP score depends on four main properties: logic of connection, predictive power, covariance between predictors and marginal predictor probabilities (biases). This paper extends that work to a broader context, in an attempt to characterize properties of discrete Bayesian networks that contribute to the presence of variables (network nodes) with high IMP scores. We have found that there is a relationship between the IMP score of a node and its territory size, i.e., its position along a pathway with one source: nodes far from the source display larger IMP scores than those closer to the source, and longer pathways display larger maximum IMP scores. This appears to be a consequence of the fact that nodes with small territory have larger probability of having highly covariate predictors, which leads to smaller IMP scores. in addition, a larger number of XOR and NXOR predictive logic relationships has positive influence over the maximum IMP score found in the pathway. This work presents analytical results based on a simple structure network and an analysis involving random networks constructed by computational simulations. Finally, results from a real Bayesian network application are provided. (C) 2012 Elsevier Inc. All rights reserved.
Assunto Bayesian network
Feature selection
Intrinsically multivariate prediction
Idioma Inglês
Financiador Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
Microsoft Research
U.S. National Science Foundation, through NSF
Número do financiamento U.S. National Science Foundation, through NSF: CCF-0845407
Data 2013-03-10
Publicado em Information Sciences. New York: Elsevier B.V., v. 225, p. 18-34, 2013.
ISSN 0020-0255 (Sherpa/Romeo, fator de impacto)
Editor Elsevier B.V.
Extensão 18-34
Fonte http://dx.doi.org/10.1016/j.ins.2012.10.027
Direito de acesso Acesso restrito
Tipo Artigo
Web of Science WOS:000314084700002
URI http://repositorio.unifesp.br/handle/11600/36084

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