Parallel ensemble of online sequential extreme learning machine based on MapReduce

Neurocomputing

Volumn 174, Issue , 2016, Pages 352-367

  Huang, Shan ^a   Wang, Botao ^a   Qiu, Junhao ^a   Yao, Jitao ^a   Wang, Guoren ^a   Yu, Ge ^a  

^a NORTHEASTERN UNIVERSITY   (China)

Author keywords

Ensemble; Extreme learning machine; MapReduce; Parallel learning; Sequential learning

Indexed keywords

ALGORITHMS; BIG DATA; CLUSTERING ALGORITHMS; KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; SCHEDULING ALGORITHMS; SOCIAL NETWORKING (ONLINE);

ENSEMBLE; EXTREME LEARNING MACHINE; MAP-REDUCE; PARALLEL LEARNING; SEQUENTIAL LEARNING;

E-LEARNING;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER SYSTEM; INTERMETHOD COMPARISON; MACHINE LEARNING; MATHEMATICAL MODEL; ONLINE SEQUENTIAL EXTREME LEARNING MACHINE; PRIORITY JOURNAL; VALIDATION PROCESS;

EID: 84940768710     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.04.105     Document Type: Article

References (25)

1. Rokach L. Ensemble-based classifiers. Artif. Intell. Rev. 2010, 33(1-2):1-39. 10.1007/s10462-009-9124-7.
2. G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, Extreme Learning Machine, Technical Report ICIS/03/2004, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, 2004.
3. Huang G.-B., Chen L. Convex incremental extreme learning machine. Neurocomputing 2007, 70:3056-3062.
4. Huang G.-B., Zhou H., Ding X., Zhang R. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man. Cybern. Part B: Cybern. 2012, 42(2):513-529. 10.1109/TSMCB.2011.2168604.
5. Lan Y., Soh Y.C., Huang G.-B. Ensemble of online sequential extreme learning machine. Neurocomputing 2009, 72(13):3391-3395.
6. Liu N., Wang H. Ensemble based extreme learning machine. IEEE Signal Process. Lett. 2010, 17(8):754-757. 10.1109/LSP.2010.2053356.
7. Zhai J.-h., Xu H.-y., Wang X.-z. Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput. 2012, 16(9):1493-1502. 10.1007/s00500-012-0824-6.
8. M. Heeswijk, Y. Miche, T. Lindh-Knuutila, P. Hilbers, T. Honkela, E. Oja, A. Lendasse, Adaptive ensemble models of extreme learning machines for time series prediction, in: C. Alippi, M. Polycarpou, C. Panayiotou, G. Ellinas (Eds.), Artificial Neural Networks C ICANN 2009, Lecture Notes in Computer Science, vol. 5769, Springer, Berlin Heidelberg, 2009, pp. 305-314 . doi:10.1007/978-3-642-04277-5_31.
9. He Y.-L., Geng Z.-Q., Xu Y., Zhu Q.-X. A hierarchical structure of extreme learning machine (helm) for high-dimensional datasets with noise. Neurocomputing 2014, 128:407-414. 〈http://www.sciencedirect.com/science/article/pii/S0925231213009004〉. 10.1016/j.neucom.2013.08.024.
10. Liang N.-Y., Huang G.-B., Saratchandran P., Sundararajan N. A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans. Neural Netw. 2006, 17(6):1411-1423. 10.1109/TNN.2006.880583.
11. T. White, Hadoop: The Definitive Guide, O'Reilly Media, Inc., Seabastopol, CA, USA, 2012.
12. He Q., Shang T., Zhuang F., Shi Z. Parallel extreme learning machine for regression based on mapreduce. Neurocomputing 2013, 102:52-58. 10.1016/j.neucom.2012.01.040.
13. J. Xiang, M. Westerlund, D. Sovilj, G. Pulkkis, Using extreme learning machine for intrusion detection in a big data environment, in: Proceedings of the 2014 Workshop on Artificial Intelligent and Security Workshop, AISec '14, ACM, New York, NY, USA, 2014, pp. 73-82 . doi:10.1145/2666652.2666664.
14. J. Xin, Z.Wang, C. Chen, L. Ding, G.Wang, Y. Zhao, Elm*: distributed extreme learning machine with mapreduce, World Wide Web, 2013, pp. 1-16 . doi:10.1007/s11280-013-0236-2.
15. Chen J., Chen H., Wan X., Zheng G. Mr-elm. a mapreduce-based framework for large-scale elm training in big data era. Neural Comput. Appl. 2014, 1-10. 10.1007/s00521-014-1559-3.
16. B. Wang, S. Huang, J. Qiu, Y. Liu, G. Wang, Parallel online sequential extreme learning machine based on mapreduce, Neurocomputing 149 (Part A) (2015) 224-232, Advances in Neural Networks Selected papers from the Tenth International Symposium on Neural Networks (ISNN 2013), Advances in Extreme Learning Machines Selected articles from the International Symposium on Extreme Learning Machines (ELM 2013). URL 〈〉. http://www.sciencedirect.com/science/article/pii/S092523121401145X.
17. R. Yan, M.-O. Fleury, M. Merler, A. Natsev, J.R. Smith,Large-scale multimedia semantic concept modeling using robust subspace bagging and mapreduce, in: Proceedings of the First ACM Workshop on Large-scale Multimedia Retrieval and Mining, LS-MMRM '09, ACM, New York, NY, USA, 2009, pp. 35-42. doi:10.1145/1631058.1631067.
18. N. K. Alham, M. Li, Y. Liu, M. Qi, A mapreduce-based distributed svm ensemble for scalable image classification and annotation, Comput. Math. Appl. 66 (10) (2013) 1920-1934, iCNC-FSKD 2012 . URL 〈〉. http://www.sciencedirect.com/science/article/pii/S0898122113004434.
19. Ju C., Zou J., Zhang Z., Wei J. Parallel ensemble classification algorithm based on the mapreduce technology (in Chinese). Telecommun. Sci. 2012, 28(7):40-47.
20. J. Dean, S. Ghemawat, Mapreduce: Simplified data processing on large clusters, Commun. ACM 51 (1) (2008) 107-113 . http://dx.doi.org/10.1145/1327452.1327492.
21. Ghemawat S., Gobioff H., Leung S.-T. The google file system. SIGOPS Oper. Syst. Rev. 2003, 37(5):29-43. 10.1145/1165389.945450.
22. K. Shvachko, H. Kuang, S. Radia, R. Chansler, The hadoop distributed file system, in: Proceedings of the 2010 IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), MSST '10, IEEE Computer Society, Washington, DC, USA, 2010, pp. 1-10 . doi:10.1109/MSST.2010.5496972.
23. Y. Kwon, M. Balazinska, B. Howe, J. Rolia, Skewtune: Mitigating skew in mapreduce applications, in: Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data, SIGMOD '12, ACM, New York, NY, USA, 2012, pp. 25-36 . doi:10.1145/2213836.2213840.
24. Q. Chen, D. Zhang, M. Guo, Q. Deng, S. Guo, Samr: A self-adaptive mapreduce scheduling algorithm in heterogeneous environment, in: 2010 IEEE 10th International Conference on Computer and Information Technology (CIT), 2010, pp. 2736-2743 . doi:10.1109/CIT.2010.458.
25. H. Arndt, M. Bundschus, A. Naegele, Towards a next-generation matrix library for java, in: 33rd Annual IEEE International Computer Software and Applications Conference, 2009, COMPSAC'09, vol. 1, IEEE, Seattle, WA,USA, 2009, pp. 460-467.