基于成员相似性的集成极端学习机

doi:10.11772/j.issn.1001-9081.2014.04.1089

计算机应用 ›› 2014, Vol. 34 ›› Issue (4): 1089-1093.DOI: 10.11772/j.issn.1001-9081.2014.04.1089

基于成员相似性的集成极端学习机

叶松林,韩飞,赵敏汝

江苏大学计算机科学与通信工程学院,江苏镇江 212013

收稿日期:2013-10-08 修回日期:2013-12-23 出版日期:2014-04-01 发布日期:2014-04-29
通讯作者: 叶松林
作者简介:叶松林(1988-),男,安徽安庆人,硕士研究生,主要研究方向:模式识别、神经网络;
韩飞(1976-),男,安徽安庆人,副教授,博士,主要研究方向:智能信息处理、进化计算;
赵敏汝(1989-),女,江苏东台人,硕士研究生,主要研究方向:神经网络、进化计算。
基金资助:
国家自然科学基金资助项目

Ensemble Extreme Learning Machine Based on the Members Similarity

YE Songlin,HAN Fei,ZHAO Minru

School of Computer Science and Communications Engineering, Jiangsu University, Zhenjiang Jiangsu 212013

Received:2013-10-08 Revised:2013-12-23 Online:2014-04-01 Published:2014-04-29
Contact: YE Songlin

摘要/Abstract

摘要：

为了增大各成员间的差异度以改善集成系统的性能，提出了一种基于成员间相似性选择的集成极端学习机(ELM)。首先，筛选出分类性能较高的备选极端学习机；其次，根据成员间的相似性运用微粒群算法(PSO)进一步选出最优的集成成员集合。通过选出相似度低的极端学习机来提高集成成员间差异度，从而有效提高集成系统的分类能力。选出的成员学习机在不同的集成规则下都具有良好性能。在四个UCI数据集上的实验结果表明，与经典的集成极端学习机相比，基于成员相似性选择的集成极端学习机具有更优的泛化性能和稳定性。

Abstract:

To increase the diversity among the selected members to enhance the performance of the ensemble system, an ensemble Extreme Learning Machine (ELM) based on the selection of members similarity named EELMBSMS was proposed. Firstly, some candidate ELMs with high classification ability were selected. Then, Particle Swarm Optimization (PSO) algorithm was used to select the optimal subset of the ensemble members according to the similarity among the members. The diversity of the selected members was improved by selecting those ELMs with low similarity, which improved the classification performance of the ensemble system effectively. The selected ELMs obtained better performance with different integration rules. The experimental results on four UCI datasets verify that EELMBSMS has better stability and better generalization than some classical ensemble extreme learning machines.

中图分类号:

TP183

叶松林韩飞赵敏汝. 基于成员相似性的集成极端学习机[J]. 计算机应用, 2014, 34(4): 1089-1093.

YE Songlin HAN Fei ZHAO Minru. Ensemble Extreme Learning Machine Based on the Members Similarity[J]. Journal of Computer Applications, 2014, 34(4): 1089-1093.

参考文献

［1］HUANG G, ZHU Q, SIEW C K. Extreme learning machine:a new learning scheme of feedforward neural networks ［C］// Proceedings of the 2004 International Joint Conference on Neural Networks. Piscataway: IEEE, 2004: 985-990.
［2］ZHAO L, TANG J, CHAI T. Soft sensor of mill load based on selective extreme learning machine ensemble ［J］. Journal of Zhejiang University: Engineering Science Edition, 2011, 45(12): 2088-2092. (赵立杰,汤健,柴天佑.基于选择性极限学习机集成的磨机负荷软测量［J］.浙江大学学报:工学版,2011,45(12):2088-2092.)
［3］LIU N, WANG H. Ensemble based extreme learning machine ［J］. IEEE Signal Processing Letters, 2010, 17(8): 754-757.
［4］LAN Y, SOH Y C, HUANG G B. Ensemble of online sequential extreme learning machine ［J］. Neurocomputing, 2009, 72(13): 3391-3395.
［5］HUANG G B, LAN Y, SOH Y C. A fast and accurate online sequential learning algorithm for feedforward networks ［J］. IEEE Transactions on Neural Networks, 2006, 17(6): 1411-1423.
［6］ZHOU Z,CHEN Z.Neural network ensemble ［J］. Chinese Journal of Computers, 2002, 25(1): 1-8. (周志华,陈志福.神经网络集成［J］.计算机学报,2002,25(1):1-8.)
［7］KENNEDY J, EBERHART R C. Particle swarm optimization ［C］// Proceedings of the 1995 IEEE International Conference on Neural Networks. Washington, DC: IEEE Computer Society, 1995:1942-1948.
［8］PARROTT D, LI X D. Locating and tracking multiple dynamic optima by a particle swarm model using speciation ［J］. IEEE Transactions on Evolutionary Computation, 2006,10(4):440-458.
［9］LANGDON W B, POLI R. Evolving problems to learn about particle swarm optimizers and other search algorithms ［J］. IEEE Transactions on Evolutionary Computation, 2007, 11(5): 561-578.
［10］SHI Y H, EBERHART R C. A modified particle swarm optimizer ［C］// Proceedings of the 1998 IEEE International Conference on Evolutionary Computation. Piscataway: IEEE, 1998: 69-73.
［11］HUANG D S, IP H H S, LAW K C K, et al.Zeroing polynomials using modified constrained neural network approach ［J］. IEEE Transactions on Neural Networks, 2005, 16(3): 721-732.
［12］HAN F, YAO H F, LING Q H. An improved evolutionary extreme learning machine based on particle swarm optimization ［J］. Neurocomputing, 2013, 116: 87-93.
［13］YANG S, HAN F, GUAN J. New gene selection method based on clustering and particle swarm optimization ［J］. Journal of Computer Applications, 2013, 33(5): 1285-1288. (杨善秀,韩飞,关键.基于聚类和微粒群优化的基因选择新方法［J］.计算机应用,2013,33(5):1285-1288.)
［14］FRANK A, ASUNCION A. UCI machine learning repository [DB/OL]. [2013-05-06]. http://archive.ics.uci.edu/ml.
［15］TAO Y. Vector based computing scheme of similarity ［J］. Journal of Yunnan Normal University, 2001, 21(5): 17-19. (陶跃华.基于向量的相似度计算方案［J］.云南师范大学学报,2001, 21(5):17-19.)
［16］ZHANG C, NI J, MIAO H, et al.Study on kernel principal component analysis method based on similarity and its application ［J］. Computer Engineering, 2011,37(14):239-241. (张传标,倪建军,苗红霞,等.基于相似度的核主元分析方法及其应用研究［J］.计算机工程,2011,37(14):239-241.)

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基于成员相似性的集成极端学习机

Ensemble Extreme Learning Machine Based on the Members Similarity

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