While the intelligent systems need parameter modeling, users often face the dilemma of scarce modeling samples. This paper proposed a BN parameter learning method-constrained data maximum entropy(CDME) algorithm for the modeling of BN parameters under the small data sets. In the case of estimating BN parameters by using small data sets, it transformed the qualitative expert knowledge into inequality constraints for the sake of generating candidate parameter sets by Bootstrap algorithm. Then it estimated the BN parameters in the light of the maximum entropy principle. The experimental results show that CDME algorithm learning effects are similar to the classical MLE algorithm when the modeling data size is sufficient. However, when the data size is limited, the parameters of BN can be modeled by using the CDME, and the learnt accuracy is superior to MLE or QMAP algorithm. It also applied CDME to a real fault diagnosis while the data set was relatively scarce. The results of the diagnosis reasoning demonstrate that the presented parameter learning approach is effective. The CDME parameter learning algorithm provides a new modeling way for BN parameter under the small data sets.

[2019-02-05] Printed Article /doc/2017.08.0868/v2