小时间序列动态完全Bayesian集成分类器研究  

Dynamic full Bayesian ensemble classifiers for small time series

作　　者：王双成 郑飞 高瑞 

机构地区：[1]上海立信会计金融学院信息管理学院,上海201620 [2]上海立信会计金融学院统计与数学学院,上海201620

出　　处：《中国科学：信息科学》2017年第11期1445-1463,共19页Scientia Sinica(Informationis)

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

摘　　要：提高连续属性小时间序列分类的可靠性重要且具有挑战性.由于小时间序列所蕴含的信息不充分和时间序列数据具有时序依赖性,使得优化分类器与数据的拟合程度非常困难,而且非时间序列数据分类器的许多成熟技术都不具有实用性.针对这种情况,本文采用动态完全Bayesian分类器来增加属性为类提供的信息量,以实现时序与非时序信息的融合,并将基于具有对角平滑参数矩阵的多元Gaussian核函数估计属性条件联合密度、平滑参数的区间划分、时序递进分类准确性标准、平滑参数配置树的构建和分类器选择与平均等相结合来建立小时间序列动态完全Bayesian集成分类器.使用宏观经济小时间序列数据集进行实验,实验的结果显示,经过优化的动态完全Bayesian集成分类器具有良好的分类准确性.Improving the reliability of small time series classifiers with continuous attributes is an important and challenging task. The information contained in small time series is not sufficient and a temporal dependency exists between data records, which makes it very difficult to optimize the fitting degree between the classifier and the data, and many mature techniques of non-time series data classifiers are not practical. We use a dynamic full Bayesian classifier to increase the amount of information provided by the attribute to the class, and realize the fusion of temporal and nonsequential information. By combining the conditional joint density estimation of attributes based on the multivariate Gaussian kernel function with a diagonal smoothing parameter matrix,the interval division of smoothing parameter values, the timing progressive classification accuracy criterion, the construction of the smoothing parameter configuration tree, classifier selection and averaging, etc, a dynamic full Bayesian ensemble classifier was established for small time series. Experiments were performed using small time series in macroeconomic analysis. The results show that the optimized dynamic full Bayesian ensemble classifiers have very good classification accuracy.

关 键 词：动态完全Bayesian分类器 多元Gaussian核函数 平滑参数 分类准确性 分类器选择与平均 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]