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

作　　者：邵良杉[1] 马寒[1] 温廷新[1] 

机构地区：[1]辽宁工程技术大学系统工程研究所,辽宁葫芦岛125105

出　　处：《计算机工程与应用》2016年第13期121-125,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.71371091);辽宁省高等学校杰出青年学者成长计划(No.LJQ2012027)

摘　　要：将极限学习机算法与旋转森林算法相结合,提出了以ELM算法为基分类器并以旋转森林算法为框架的RF-ELM集成学习模型。在8个数据集上进行了3组预测实验,根据实验结果讨论了ELM算法中隐含层神经元个数对预测结果的影响以及单个ELM模型预测结果不稳定的缺陷;将RF-ELM模型与单ELM模型和基于Bagging算法集成的ELM模型相比较,由稳定性和预测精度的两组对比实验的实验结果表明,对ELM的集成学习可以有效地提高ELM模型的性能,且RF-ELM模型较其他两个模型具有更好的稳定性和更高的准确率,验证了RF-ELM是一种有效的ELM集成学习模型。A RF-ELM classifier ensemble model is proposed, which both combines rotation forest algorithm and extremelearning machine algorithm. This model utilizes extreme learning machine to be base classifier and rotation forest algorithmto be integration framework. Three experiments are completed on eight data sets. According to the experimentalresults, the effects of hidden layer neurons number to forecast results and the instability defects of a single ELM predictionmodel are discussed. The results of contrast experiment for stability and prediction accuracy demonstrate that ensemblelearning model for ELM algorithm can improve its performance and RF-ELM has a better stability and precision thanELM algorithm and Bagging-ELM model. RF-ELM model is proved to be an effective classifier ensemble learning modelfor ELM algorithm.

关 键 词：极限学习机 旋转森林 分类器集成 BAGGING算法 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]