Naie Bayes Ensemble Learning Based on Oracle Selection

摘要：

Aiming at the stability of Naie Bayes algorithm and overcoming the limitation of the attributes independence assumption in the Naive Bayes learning, we present an ensemble learning algorithm for naive Bayesian classifiers based on oracle selection (OSBE). Firstly we weaken the stability of the naive Bayes with oracle strategy, then select the better classifier as the component of ensemble of the naive Bayesian classifiers, finally integrate the classifiers?results with voting method. The experiments show that OSBE ensemble algorithm obviously improves the generalization performance which is compared with the Naie Bayes learning. And it prove in some cases the OSBE algorithm have better classification accuracy than Bagging and Adaboost.

关键词： Naie Bayes Ensemble Oracle Stability Diversity and Vote

作者: Kai Li Lifeng Hao

作者单位: School of Mathematics and Computer, Hebei University, Baoding 071002 China Key Lab. In Machine Learning and Computational Intelligence of Hebei Province, Baoding 071002 China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 665-670

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）