计算机应用与软件2016，Vol.33Issue(9)：279-283,5.DOI:10.3969/j.issn.1000-386x.2016.09.065
选择性集成极限学习机分类器建模研究
RESEARCH ON MODELLING SELECTIVE ENSEMBLE EXTREME LEARNING MACHINE CLASSIFIER
摘要
Abstract
As its advantage,the training speed of extreme learning machine (ELM)is extremely fast.But sometimes its stability and precision can’t meet the requirement of practical application.In order to solve the problem,this paper introduces a solution for ELMwhen to be used in classification,in it the output weight matrix is improved with the evaluation factor of information in training results.Meanwhile, the hidden layer output matrixes competitive mechanism is added to improve the stability of ELM.For the sake of further improving ELM’s accuracy rate in classification,we propose a kind of selective ensemble extreme learning machine classifier by learning from the theory of neural network ensemble.In ensemble method,we adopt the improved Bagging and propose a subnet’s parameter vector-based similarity evaluation method and selective ensemble policy.Finally it is demonstrated by UCI data test that compared with Bagging and traditional all ensemble ELM,the solution proposed here has better performance in classification.关键词
极限学习机/神经网络/选择性集成/BaggingKey words
Extreme learning machine/Neural network/Selective ensemble/Bagging分类
信息技术与安全科学引用本文复制引用
徐晓杨,纪志成..选择性集成极限学习机分类器建模研究[J].计算机应用与软件,2016,33(9):279-283,5.基金项目
国家粮食局公益性科研项目（201313012）。 （）
