小时间序列的动态朴素贝叶斯分类器学习与优化  

Learning and optimization of dynamic naive Bayesian classifiers for small time series

作　　者：王双成[1,2] 高瑞[1] 杜瑞杰[1] 

机构地区：[1]上海立信会计学院数学与信息学院,上海201620 [2]上海立信会计学院立信会计研究院,上海201620

出　　处：《控制与决策》2017年第1期163-166,共4页Control and Decision

基　　金：国家自然科学基金项目(61272209);上海市自然科学基金项目(15ZR1429700)

摘　　要：小时间序列在宏观经济领域普遍存在,对小时间序列的分类预测也有着广泛的需求.由于小时间序列蕴含的信息不充分,有效地提高小时间序列分类预测的可靠性非常困难,目前也缺少这方面的研究.针对这种情况,在基于引入平滑参数的高斯核函数估计属性边缘密度的基础上,建立用于小时间序列分类预测的动态朴素贝叶斯分类器,并给出平滑参数的同步和异步优化方法.实验结果表明,优化能够显著提高小时间序列分类预测的准确性.The small time series exists generally in the field of macroeconomy. There are wide demands for the classification of small time series in macroeconomy. Because the information contained in the small time series is not sufficient, it is very difficult to effectively improve the reliability of small time series classification. In view of this situation, on the basis of using the Gaussian kernel function of introducing the smoothing parameter to estimate the attribute marginal density, the dynamic naive Bayesian classifier for small time series classification is presented, and the synchronous and asynchronous optimization method for smoothing parameters are given. The experimental results show that the classification accuracy of the small time series classifier can be improved significantly by optimization.

关 键 词：贝叶斯网络 分类器 时间序列 高斯核函数 平滑参数 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]