连续属性一阶贝叶斯衍生分类器学习与集成  

Learning and Ensemble of One-Order Bayesian Derivative Classifiers with Continuous Attributes

作　　者：王双成[1,2] 高瑞[1,3] 冷翠平[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 [3]上海财经大学统计与管理学院,上海200433

出　　处：《模式识别与人工智能》2015年第6期499-506,共8页Pattern Recognition and Artificial Intelligence

基　　金：上海市自然科学基金项目(No.15ZR1429700);上海市教委科研创新项目(No.15ZZ099)资助

摘　　要：依据copula和贝叶斯网络理论,将高斯copula函数、引入平滑参数的高斯核函数和以分类器的分类准确性为标准的属性父结点贪婪选择等相结合,综合考虑效率和可靠性,进行连续属性一阶贝叶斯衍生分类器学习、优化和集成.使用UCI数据库中连续属性分类数据进行实验,结果显示,经过优化和集成的一阶连续属性贝叶斯衍生分类器具有良好的分类准确性.Considering the efficiency and reliablity, Gaussian copula function, Gaussian kernel function with smoothing parameter, the criteria of classification accuracy and the greedy selection of attribute parent node are combined to carry out the learning, optimization and ensemble of one-order Bayesian derivative classifiers with continuous attributes. Experiment and analysis are executed by using datasets with continuous attributes in UCI dataset. The results show that the classifiers after optimization and ensemble have good classification accuracy.

关 键 词：贝叶斯衍生分类器 高斯copula函数 分类器集成 贝叶斯网络 连续属性 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]