References

Anbalagan, P. and R.M. Chandrasekaran, 2015. A parallel weighted decision tree classifier for complex spatial landslide analysis: Big data computation approach. Int. J. Comput. Appl., 124: 5-9.
Direct Link  |  

Ding, L., Y. Liu, B. Song and J. Xin, 2015. Efficient ELM-based two stages query processing optimization for big data. Math. Prob. Eng., 2015: 1-12.
Direct Link  |  

Divya, A.J. and S. Gagandeep, 2015. Classification of big data through artificial intelligence. Int. J. Comput. Sci. Mobile Comput., 4: 17-25.
Direct Link  |  

Grolinger, K., M. Hayes, W.A. Higashino, L.A. Heureux and D.S. Allison et al., 2014. Challenges for mapreduce in big data. Proceeding of the 2014 IEEE World Congress on Services, June 27-July 2, 2014, IEEE, Ottawa,Canada, ISBN:978-1-4799-5069-0, pp: 182-189.

Horta, E.G., C.L.D. Castro and A.P. Braga, 2015. Stream-based extreme learning machine approach for big data problems. Math. Prob. Eng., 2015: 1-17.
Direct Link  |  

Hualong, Y.U., S. Changyin, Y. Wankou, Y. Xibei and X. Zuo, 2015. One uncertainty-based active learning algorithm using extreme learning machine. Neurocomputing, 166: 140-150.

Huang, G.B., D.H. Wang and Y. Lan, 2011. Extreme learning machines: A survey. Int. J. Mach. Learn. Cybern., 2: 107-122.
CrossRef  |  Direct Link  |  

Huang, G.B., H. Zhou, X. Ding and R. Zhang, 2012. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B (Cybern.), 42: 513-529.
CrossRef  |  Direct Link  |  

Lopez, V., D.S. Rio, J.M. Benitez and F. Herrera, 2014. On the use of map reduce to build linguistic fuzzy rule based classification systems for big data. Proceeding of the 2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), July 6-11, 2014, IEEE, Granada, Spain, ISBN:978-1-4799-2072-3, pp: 1905-1912.

Miche, Y., A. Akusok, D. Veganzones, K.M. Bjork and E. Severin et al., 2015. SOM-ELM-self-organized clustering using ELM. Neurocomputing, 165: 238-254.
Direct Link  |  

Paakkonen, P. and D. Pakkala, 2015. Reference architecture and classification of technologies, products and services for big data systems. Big Data Res., 2: 166-186.
Direct Link  |  

Rebentrost, P., M. Mohseni and S. Lloyd, 2014. Quantum support vector machine for big data classification. Phys. Rev. Lett., Vol. 113,

Rizk, Y. and M. Awad, 2015. On the distributed implementation of unsupervised extreme learning machines for big data. Procedia Comput. Sci., 53: 167-174.

Slavakis, K., G.B. Giannakis and G. Mateos, 2014. Modeling and optimization for big data analytics: (Statistical) learning tools for our era of data deluge. IEEE. Signal Process. Mag., 31: 18-31.
CrossRef  |  Direct Link  |  

Sun, Y., Y. Yuan and G. Wang, 2011. An OS-ELM based distributed ensemble classification framework in P2P networks. Neurocomputing, 74: 2438-2443.

Tekin, C. and V.D.M. Schaar, 2013. Distributed online big data classification using context information. Proceeding of the 2013 51st Annual Allerton Conference on Communication, Control and Computing (Allerton), October 2-4, 2013, IEEE, California, USA., ISBN:978-1-4799-3410-2, pp: 1435-1442.

Triguero, I., D. Peralta, J. Bacardit, S. Garcia and F. Herrera, 2015. A map reduce solution for prototype reduction in big data classification. Neurocomputing, 150: 331-345.
Direct Link  |  

Triguero, I., M. Galar, S. Vluymans, C. Cornelis and H. Bustince et al., 2015. Evolutionary undersampling for imbalanced big data classification. Proceeding of the 2015 IEEE Congress on Evolutionary Computation (CEC), May 25-28, 2015, IEEE, Brussels, Belgium, ISBN: 978-1-4799-7492-4, pp: 715-722.

Xin, J., Z. Wang, L. Qu and G. Wang, 2015. Elastic extreme learning machine for big data classification. Neurocomputing, 149: 464-471.
Direct Link  |