An ensemble learning algorithm based on generalized attribute value partitioning
The method of disturbing training data randomly to train individual classifiers has been widely applied in some ensemble learning methods such as Bagging and Boosting to achieve strong generalization ability, however, it seems something blind. In this paper, a new ensemble learning algorithm named GAVPEL is proposed. By using the hierarchy nature of the data set, GAVPEL leverages the generalized attribute value partitioning method to form an ensemble tree, called a generalized classifier hierarchy tree. While classifying, GAVPEL selects part of the individual classifiers based on attribute value and ensembles them with majority voting. Experiment results show that GAVPEL can efficiently improve generalization performance when compared with some popular ensemble learning algorithms.
ensemble learning selective ensemble knowledge granularity generalization ability
Weidong Tian Fang Wu Jipeng Qiang Hongjuan Zhou
School of Computer & Information, Hefei University of Technology, Hefei, China 230009 Institute of I School of Computer & Information, Hefei University of Technology, Hefei, China 230009
国际会议
桂林
英文
1-7
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）