Ensembling Base Classifiers To Improve Predictive Accuracy

摘要：

　　The algorithm of ensembling base classifiers can improve predictive accuracy,and achieve a better generalization.However,the ensemble classificition methods in literature have been used in more rule-based algorithms of classifier.This paper presents a novel algorithm: CVCEEP(Classification by Voting Classifiers based on Essential Emerging Patterns).By learning the method of Bagging,multiple base-classifiers were generated on different bootstrap samples and combined as a powerful classifier by voting.Experimental results show that CVCEEP achieve a better predictive accuracy and can be match to the classic classification algorithms that we have known.

关键词： ensemble learning classification emerging patter

作者: Wen Qingdi

作者单位: Department of Information Guizhou University of Finance and Economics Guiyang China

会议类型: 国际会议

会议名称: The 14th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2015)(第十四届分布式计算及其应用国际学术研讨会)

会议地点: 贵阳

会议语种:英文

页码: 268-271

在线出版日期: 2015-08-18（万方平台首次上网日期，不代表论文的发表时间）