计算机工程与应用2016，Vol.52Issue(13)：121-125,5.DOI:10.3778/j.issn.1002-8331.1408-0096
极限学习机的分类器集成模型研究
Study on classifier ensemble learning model for extreme learning machine
摘要
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 algo-rithm 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.关键词
极限学习机/旋转森林/分类器集成/Bagging算法Key words
Extreme Learning Machine(ELM)/Rotation Forest(RF)/classifier ensemble/Bagging algorithm分类
信息技术与安全科学引用本文复制引用
邵良杉,马寒,温廷新..极限学习机的分类器集成模型研究[J].计算机工程与应用,2016,52(13):121-125,5.基金项目
国家自然科学基金(No.71371091) （No.71371091）
辽宁省高等学校杰出青年学者成长计划(No.LJQ2012027). （No.LJQ2012027）
