CAJ | 학술논문

基于数据学习的贝叶斯网络结构学习算法因搜索空间大而效率低。领域专家可根据自己的经验知识确定网络结构中固有的因果关系。通过收集不同专家的意见，并利用证据理论进行综合，确定其中的部分因果关系，去除其中无意义的因果关系，然后利用常用的学习算法进行学习，减小搜索空间，提高算法效率。实验结果表明基于专家知识融合的贝叶斯网络构造方法利用专家知识来限制学习算法的搜索条件，有效地缩小了搜索空间，利用证据理论综合多个专家知识，防止了单个专家的主观片面性，能够有效地提高学习效率。
기우수거학습적패협사망락결구학습산법인수색공간대이효솔저。영역전가가근거자기적경험지식학정망락결구중고유적인과관계。통과수집불동전가적의견，병이용증거이론진행종합，학정기중적부분인과관계，거제기중무의의적인과관계，연후이용상용적학습산법진행학습，감소수색공간，제고산법효솔。실험결과표명기우전가지식융합적패협사망락구조방법이용전가지식래한제학습산법적수색조건，유효지축소료수색공간，이용증거이론종합다개전가지식，방지료단개전가적주관편면성，능구유효지제고학습효솔。
The efficiency of Bayesian networks structure learning algorithm based on data learning is low due to the big search space. Domain experts determine the inherent causal relationship in network structure according to their own experi-ence knowledge. By collecting different expert opinion, and using the evidence theory to synthesize, it determines the part of causality, removes some meaningless causal relationship, then uses the common learning algorithms to learn, reduces the search space, enhances the efficiency of the algorithm. The experimental results show that the Bayesian network construction method based on the expert knowledge fusion limits the search terms of the learning algorithm by expert knowledge, effectively reduces the search space, uses the evidence theory to synthesize much expert knowledge, prevents a single expert subjective and one-sidedness, can effectively improve the learning efficiency.