加权粗糙集算法的PDA图书馆最优选书策略  

Most Preferred Strategy of PDA Library Book Selection Based on Weighted Rough Algorithm

作　　者：陈蕾[1] 杨省伟[2] 

机构地区：[1]河南理工大学图书馆,河南焦作454003 [2]河南质量工程职业学院现代教育技术中心,河南平顶山467000

出　　处：《科技通报》2015年第7期152-155,共4页Bulletin of Science and Technology

摘　　要：针对标准贝叶斯网络模型在PDA图书馆选书分析的应用中表现出误差较大的问题,本文提出了一种基于加权粗糙集优化贝叶斯网络的PDA选书模型,首先采用粗糙集的方式,重构属性集合,用新的属性集合代替原来的属性集合,然后采用属性序描述方法,这样得到的约简后的条件属性将按照对决策属性影响逐渐减小的顺序依次排列,为了减小此缺陷的影响,采用加权的方式对基于粗糙集的贝叶斯模型进行改进。仿真试验结果表明,基于加权粗糙集优化贝叶斯网络的PDA选书系统功能完善,并且通过实际选书案例证明其选书精确性较高。Bayesian network model for a standard library book selection in PDA applications analysis showed large errors, this paper presents an optimization model based on Bayesian network PDA choosing books based on the weighted rough set, first using rough set way, reconstructed collection of properties, with a new set of properties instead of the original set of attributes, and then use the properties described sequence method, the condition attribute reduction thus obtained will be arranged in sequential order after attribute effect on the decision in accordance with decreasing order, in order to reduce little effect this defect, weighted manner Bayesian model based on rough set to improve. The simulation results show that Bayesian network optimization based on the weighted rough set book selection system fully functional PDA and Selected Stories by actually choosing books to prove its high accuracy.

关 键 词：贝叶斯网络模型 PDA选书策略 粗糙集 加权优化 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]