Li Hongru, Guo Huiping
Information Science and Engineering, Northeastern University, P.O. Box 135, No. 11 St. 3, Wenhua Road, Heping District, Shenyang 110819, China.
Entropy (Basel). 2018 Aug 20;20(8):620. doi: 10.3390/e20080620.
Bayesian network structure learning from data has been proved to be a NP-hard (Non-deterministic Polynomial-hard) problem. An effective method of improving the accuracy of Bayesian network structure is using experts' knowledge instead of only using data. Some experts' knowledge (named here explicit knowledge) can make the causal relationship between nodes in Bayesian Networks (BN) structure clear, while the others (named here vague knowledge) cannot. In the previous algorithms for BN structure learning, only the explicit knowledge was used, but the vague knowledge, which was ignored, is also valuable and often exists in the real world. Therefore we propose a new method of using more comprehensive experts' knowledge based on hybrid structure learning algorithm, a kind of two-stage algorithm. Two types of experts' knowledge are defined and incorporated into the hybrid algorithm. We formulate rules to generate better initial network structure and improve the scoring function. Furthermore, we take expert level difference and opinion conflict into account. Experimental results show that our proposed method can improve the structure learning performance.
从数据中学习贝叶斯网络结构已被证明是一个NP难（非确定性多项式难）问题。提高贝叶斯网络结构准确性的一种有效方法是使用专家知识，而不是仅使用数据。一些专家知识（在此称为显性知识）可以使贝叶斯网络（BN）结构中节点之间的因果关系清晰，而其他知识（在此称为模糊知识）则不能。在先前用于BN结构学习的算法中，只使用了显性知识，但被忽略的模糊知识也是有价值的，并且在现实世界中经常存在。因此，我们基于混合结构学习算法（一种两阶段算法）提出了一种使用更全面专家知识的新方法。定义了两种类型的专家知识并将其纳入混合算法中。我们制定规则以生成更好的初始网络结构并改进评分函数。此外，我们考虑了专家水平差异和意见冲突。实验结果表明，我们提出的方法可以提高结构学习性能。
