贝叶斯网参数学习中连续变量离散化方法研究

连续变量离散化是贝叶斯网络参数学习中面临的一个重要问题,它的好坏将直接影响到贝叶斯网络的推理效果。目前缺少一种有效的手段用于评价连续变量离散化的好坏,通过研究,提出了推理信息量的概念,并采用作为衡量连续变量离散化好坏的标准。在连续变量离散化的过程中,采用遗传算法通过迭代的方式寻求最优解,其中,推理信息量作为衡量个体适应度的标准。实例分析证明,推理信息量大的推理效果好要优于推理信息量小的推理效果。

A Discretization Method of Continuous Variable in Bayesian Network Parameter Learning

Discretization of continuous variable is a very important problem in Bayesian network parameter learning,and it affects the effectiveness of Bayesian network reasoning directly.There is a lack of standard used to evaluate discretization of continuous variable at present,therefore a notion of reasoning information is presented here by study ,which is used as the measure standard of discretization.According to the simple Bayesian network,a discretization model is built,and the optimal solution is found by GA ...