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基于推理信息量的BN参数学习变量离散化方法

Discretization Method of BN Parameter Learning Variable Based on Reasoning Information
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摘要 提出推理信息量的概念,将其作为贝叶斯网络连续变量离散化评价标准。在连续变量离散化的过程中,采用遗传算法寻求最优解,设计个体编码方式、交叉算子和变异算子,将推理信息量作为衡量个体适应度的标准。实例分析证明,通过该方法对变量进行离散化后学习得到的贝叶斯网络在推理时能得到更大的推理信息量。 The concept of reasoning information is presented, which is used as the measure of discretization of continuous variables in Bayesian Network（BN）. Genetic algorithm is used to search the best solution. Encoding method, crossover operator and mutation operator is proposed. Reasoning information is used as the function of individual fitness. Experiment proves that the Bayesian Network learning from data based on this discretization method can get more reasoning information.
作者 王磊 周旋 朱延广 杨峰
机构地区 国防科技大学信息系统与管理学院
出处 《计算机工程》 CAS CSCD 北大核心 2009年第5期185-187,199,共4页 Computer Engineering
关键词 参数学习 推理信息量 离散化方法 遗传算法 parameter learning reasoning information： discretization method genetic algorithm
分类号 TP393 [自动化与计算机技术—计算机应用技术]
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参考文献5

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共引文献17

计算机工程
2009年 第5期

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