计算机应用研究2016，Vol.33Issue(3)：689-692,4.
基于贝叶斯网络的海量数据多维分类学习方法研究
Bayesian net based multi-dimensional classification learning algorithm
摘要
Abstract
In order to improve the execution efficiency of multi-dimensional classification while preserving high prediction ac-curacy,this paper proposed a Bayesian net based multi-dimensional classification learning algorithm.Firstly,it described the problem of multi-dimensional classification as the problem of conditional probability distribution.Secondly,it built a condi-tional tree Bayesian net model according to the dependence of class vector.Finally,it learnt the structure and parameters of the conditional tree model based on the training data set,and proposed a multi-dimensional classification prediction algorithm. Massive experiments on real dataset show that,compared with the state-of-the-art multi-dimensional classification algorithm MMOC,the proposed algorithm improves the execution efficiency of multi-dimensional classification while preserving high pre-diction accuracy.So,the proposed algorithm is more suitable in multi-dimensional classification for massive data.关键词
多维分类/贝叶斯网络/机器学习/海量数据Key words
multi-dimensional classification/Bayesian network/machine learning/massive data分类
信息技术与安全科学引用本文复制引用
陈池梅,张林..基于贝叶斯网络的海量数据多维分类学习方法研究[J].计算机应用研究,2016,33(3):689-692,4.基金项目
国家自然科学基金资助项目 （）
