摘要
已建成的Bayesian网与领域环境间可能存在较大偏差,加之领域本身固有的动态变化特性,因此在观察到新数据时,改善Bayesian网的性能和优化网络结构是十分必要的.提出了一种基于遗传算法的Bayesian网(包含结构和参数)求精算法.该算法基于上次的求精结果把已有的不完备数据转化成完备数据,以期望充分统计因子作为已有数据的主要存储形式,基于本次求精过程中的当前最佳个体对新数据进行完备化,并由遗传操作综合利用新数据和已有数据进行求精.模拟实验结果表明,该增量学习算法能较有效地从不完备数据中求精Bayesian网.
Because of great difference in constructed model and changes in the dynamics of the domains, it is necessary to improve the performance and accuracy of a Bayesian network as new data is observed. A genetic algorithm is introduced to refine Bayesian networks in which both parameters and structure are expected to change. The genetic operators iteratively refine a Bayesian network based on ‘goodness＇ evaluated by fitness function. This function contains three parts, exactitude that a Bayesian network respectively matches the old data and the new data, and conciseness of the model. To make computation feasible, the old incomplete data is converted into complete data based on expectation theory via the learned Bayesian network from the old data, and the new incomplete data is completed based on the available best Bayesian network in the last genetic iteration. Besides, the old data is compressed to expected sufficient statistics instead of some Bayesian networks. It not only economizes storage but also reduces the complexity of computation. Experimental results show this algorithm can make good choice between quality of the result and quantity of storage, can effectively refine Bayesian network structure from incomplete data.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2005年第9期1461-1466,共6页
Journal of Computer Research and Development
基金
国家自然科学基金项目(60303009
60496324)~~
关键词
增量学习
BAYESIAN网
不完备数据
数学期望
遗传算法
increnrnental learning
Bayesian networks
incomplete data
mathematical expectation
genetic algorithm
