关键词: 极限学习机, 旋转森林, 分类器集成, Bagging算法

Abstract: A RF-ELM?classifier ensemble model is proposed, which both combines rotation forest algorithm and extreme learning machine algorithm. This model utilizes extreme learning machine to be base classifier and rotation forest algorithm to be integration framework. Three experiments are completed on eight data sets. According to the experimental results, the effects of hidden layer neurons number to forecast results and the instability defects of a single ELM prediction model are discussed. The results of contrast experiment for stability and prediction accuracy demonstrate that ensemble learning model for ELM algorithm can improve its performance and RF-ELM has a better stability and precision than ELM algorithm and Bagging-ELM model. RF-ELM model is proved to be an effective classifier ensemble learning model for ELM algorithm.

Key words: Extreme Learning Machine（ELM）, Rotation Forest（RF）, classifier ensemble, Bagging algorithm