Learning Bayesian network parameters under new monotonic constraints#br#

doi:10.21629/JSEE.2017.06.22

Journal of Systems Engineering and Electronics ›› 2017, Vol. 28 ›› Issue (6): 1248-1255.doi: 10.21629/JSEE.2017.06.22

• • 上一篇

出版日期:2017-12-27 发布日期:2017-12-27

Learning Bayesian network parameters under new monotonic constraints#br#

Ruohai Di, Xiaoguang Gao*, and Zhigao Guo

School of Electronic and Information, Northwestern Polytechnical University, Xi’an 710129, China

Online:2017-12-27 Published:2017-12-27

摘要/Abstract

Abstract:

When the training data are insufficient, especially when only a small sample size of data is available, domain knowledge will be taken into the process of learning parameters to improve the performance of the Bayesian networks. In this paper, a new monotonic constraint model is proposed to represent a type of common domain knowledge. And then, the monotonic constraint estimation algorithm is proposed to learn the parameters with the monotonic constraint model. In order to demonstrate the superiority of the proposed algorithm, series of experiments are carried out. The experiment results show that the proposed algorithm is able to obtain more accurate parameters compared to some existing algorithms while the complexity is not the highest.

. [J]. Journal of Systems Engineering and Electronics, 2017, 28(6): 1248-1255.

Ruohai Di, Xiaoguang Gao, and Zhigao Guo. Learning Bayesian network parameters under new monotonic constraints#br#[J]. Journal of Systems Engineering and Electronics, 2017, 28(6): 1248-1255.

Learning Bayesian network parameters under new monotonic constraints#br#

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