This paper proposes a general formalism for evaluating hybrid Bayesian networks. The formalism approximates a hybrid Bayesian network into the form, called fuzzy partial least-squares Bayesian network (FPBN). The form replaces each continuous variable whose descendants include discrete variables by a partner discrete variable and adding a directed link from that partner discrete variable to the continuous one. The partner discrete variable is acquired by the discretization of the original continuous variable with a fuzzification algorithm based on the structure adaptive-tuning neural network model. In addition, the dependence between the partner discrete variable and the original continuous variable is approximated by fuzzy sets, and the dependence between a continuous variable and its continuous and discrete parents is approximated by a conditional Gaussian regression (CGR) distribution in which partial least-squares (PLS) is proposed as an alternative method for computing the vector of regression parameter. The experimental results are included to demonstrate the performances of the new approach. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Heng, X. C., & Qin, Z. (2005). FPBN: A new formalism for evaluating hybrid Bayesian networks using fuzzy sets and partial least-squares. In Lecture Notes in Computer Science (Vol. 3645, pp. 209–217). Springer Verlag. https://doi.org/10.1007/11538356_22
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