计算机应用与软件2012，Vol.29Issue(2)：57-59,3.
动态朴素贝叶斯网络分类器的特征子集选择
FEATURE SUBSET SELECTION FOR DYNAMIC NAIVE BAYESIAN NETWORK CLASSIFIER
摘要
Abstract
Classification accuracy is the most important performance indicator of classifiers. Feature subset selection is an effective method for improving the classification accuracy of classifiers. Existing methods of feature subset selection are mainly for static classifiers, while the research on dynamic classifier feature subset selection is rare. In this paper, the dynamic naive Bayesian network classifier with continuous attributes and the accuracy evaluation criterion for dynamic classification are presented first. A selection method of feature subset of dynamic naive Bayesian network classifier is developed based on this, while the actual macroeconomic time series data are used to carry out the experiments and analyses.关键词
动态朴素贝叶斯网络/分类器/特征子集选择/高斯核函数Key words
Dynamic naive bayesian network/Classifier/Feature subset selection/Gaussian kernel function分类
信息技术与安全科学引用本文复制引用
余民杰,王双成,杜瑞杰..动态朴素贝叶斯网络分类器的特征子集选择[J].计算机应用与软件,2012,29(2):57-59,3.基金项目
国家自然科学基金(60675036) （60675036）
教育部人文社科基金(09YJA630099) （09YJA630099）
上海市教委重点学科建设项目(J51702) （J51702）
上海市教委科研创新重点项目(09zz202). （09zz202）
