Abstract
Feedforward neural networks (FFNN) has been used for machine learning researches, and it really has a wide acceptance. It was noted in the recent time that feedforward neural network is far slower than required. This has created a serious bottleneck in its applications. Extreme Learning Machines (ELM) had been proposed as alternative learning algorithm to FFNN, which is characterized by single-hidden layer feedforward neural networks (SLFN). It randomly chooses hidden nodes and determines their output weight analytically. This paper review is to provide a roadmap for ELM as an efficient research tool in machine learning with the aim of finding research gap into further study. It was discovered through this study that research publications in ELM continues to grow yearly from 16.20% in 2013 to 40.83% in 2016.
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References
Huang, G., Zhu, Q., Siew, C.: Extreme learning machine: a new learning scheme of feedforward neural networks. In: IEEE International Joint Conference Neural Networks, vol. 2, pp. 985–990 (2004). doi:10.1109/IJCNN.2004.1380068
Bin, H.G.: What are extreme learning machines? Filling the gap between Frank Rosenblatt’s dream and John von Neumann’s puzzle. Cognit. Comput. 7, 263–278 (2015). doi:10.1007/s12559-015-9333-0
Huang, G., Bin, H.G., Song, S., You, K.: Trends in extreme learning machines: a review. Neural Networks 61, 32–48 (2015). doi:10.1016/j.neunet.2014.10.001
Lazarevska, L.: Wind speed prediction with extreme learning machine, pp. 154–159 (2016)
Yang, Y., Wu, Q.M.J., Member, S.: Extreme learning machine with subnetwork hidden nodes for regression and classification. IEEE Trans. Cybern. 46, 2885–2898 (2016)
Balasundaram, S., Gupta, D.: Knowledge-based extreme learning machines. Neural Comput. Appl. 27, 1629–1641 (2016). doi:10.1007/s00521-015-1961-5
Musikawan, P., Sunat, K., Chiewchanwattana, S., et al.: Improved convex incremental extreme learning machine based on ridgelet and PSO algorithm (2016)
Deng, W.Y., Bai, Z., Bin, H.G., Zheng, Q.H.: A fast SVD-hidden-nodes based extreme learning machine for large-scale data analytics. Neural Networks 77, 14–28 (2016). doi:10.1016/j.neunet.2015.09.003
Mahmood, S.F., Marhaban, M.H., Rokhani, F.Z., et al.: FASTA-ELM: a fast adaptive shrinkage/thresholding algorithm for extreme learning machine and its application to gender recognition. Neurocomputing (2016). doi:10.1016/j.neucom.2016.09.046
Liu, D., Wu, Y.X., Jiang, H.: FP-ELM: an online sequential learning algorithm for dealing with concept drift. Neurocomputing 207, 322–334 (2015). doi:10.1016/j.neucom.2016.04.043
Iosifidis, A., Tefas, A., Pitas, I.: Graph embedded extreme learning machine. IEEE Trans. Cybern. 46, 311–324 (2016). doi:10.1109/TCYB.2015.2401973
Zhang, J., Ding, S., Zhang, N., Shi, Z.: Incremental extreme learning machine based on deep feature embedded. Int. J. Mach. Learn. Cybern. 7, 111–120 (2016). doi:10.1007/s13042-015-0419-5
Liu, X., Wang, L., Huang, G.-B., et al.: Multiple kernel extreme learning machine. Neurocomputing 149, 253–264 (2015). doi:10.1016/j.neucom.2013.09.072
Yu, W., Zhuang, F., He, Q., Shi, Z.: Learning deep representations via extreme learning machines. Neurocomputing 149, 308–315 (2015). doi:10.1016/j.neucom.2014.03.077
Mao, W., Wang, J., Wang, L.: Online sequential classification of imbalanced data by combining extreme learning machine and improved SMOTE algorithm. In: Proceeding of the International Joint Conference on Neural Networks (2015). doi:10.1109/IJCNN.2015.7280620
Li, S., You, Z., Guo, H., et al.: Inverse-Free extreme learning machine with optimal information updating. IEEE Trans. Cybern. 46, 1229–1241 (2016)
Yadav, B., Ch, S., Mathur, S., Adamowski, J.: Discharge forecasting using an online sequential extreme learning machine (OS-ELM) model: a case study in Neckar River, Germany. Meas. J. Int. Meas. Confed. 92, 433–445 (2016). doi:10.1016/j.measurement.2016.06.042
Huang, G.-B., Zhu, Q., Siew, C., et al.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006). doi:10.1016/j.neucom.2005.12.126
Liu, X., Lin, S., Fang, J., Xu, Z.: Is extreme learning machine feasible? A theoretical assessment (part I). IEEE Trans. Neural Netw. Learn. Syst. 26, 7–20 (2015). doi:10.1109/TNNLS.2014.2335212
Liu, X., Lin, S., Fang, J., Xu, Z.: Is extreme learning machine feasible? A theoretical assessment (Part II). IEEE Trans. Neural Netw. Learn. Syst. 26, 7–20 (2015). doi:10.1109/TNNLS.2014.2335212
Cao, J., Lin, Z., Bin, H.G., Liu, N.: Voting based extreme learning machine. Inf. Sci. (Ny) 185, 66–77 (2012). doi:10.1016/j.ins.2011.09.015
Hu, X., Lin, H., Li, S., Sun, B.: Global and local features based classification for bleed-through removal. Sens. Imaging 17, 9 (2016). doi:10.1007/s11220-016-0134-7
Zhang, J., Feng, L., Wu, B.: Local extreme learning machine: local classification model for shape feature extraction. Neural Comput. Appl. 27, 2095–2105 (2016). doi:10.1007/s00521-015-2008-7
Ebtehaj, I., Bonakdari, H., Shamshirband, S.: Extreme learning machine assessment for estimating sediment transport in open channels. Eng. Comput. 32, 1–14 (2016). doi:10.1007/s00366-016-0446-1
Mundher Yaseen, Z., Jaafar, O., Deo, R.C., et al.: Boost stream-flow forecasting model with extreme learning machine data-driven: a case study in a semi-arid region in Iraq. J. Hydrol. 542, 603–614 (2016). doi:10.1016/j.jhydrol.2016.09.035
Badrzadeh, H., Sarukkalige, R., Jayawardena, A.W.: Hourly runoff forecasting for flood risk management: application of various computational intelligence models. J. Hydrol. (2015). doi:10.1016/j.jhydrol.2015.07.057
Ding, S.F., Xu, X.Z., Nie, R.: Extreme learning machine and its applications. Neural Comput. Appl. 25, 549–556 (2014). doi:10.1007/s00521-013-1522-8
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. 42, 513–529 (2012). doi:10.1109/TSMCB.2011.2168604
Sakakura, Y.: Extreme Learning Machine (ELM), pp. 1–14 (2013)
Zhang, L., Li, J., Lu, H.: Saliency detection via extreme learning machine. Neurocomputing 218, 103–112 (2016). doi:10.1016/j.neucom.2016.08.066
Oneto, L., Bisio, F., Cambria, E., Anguita, D.: Statistical learning theory and ELM for big social data analysis. IEEE Comput. Intell. Mag. 11, 45–55 (2016). doi:10.1109/MCI.2016.2572540
Wang, H., Xu, Z., Pedrycz, W.: An overview on the roles of fuzzy set techniques in big data processing Trends, challenges and opportunities. Knowledge-Based Syst 118, 1–16 (2016). doi:10.1016/j.knosys.2016.11.008
Bodyanskiy, Y., Vynokurova, O., Pliss I, et al.: Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks, pp. 257–262 (2016)
Bin, H.G.: An insight into extreme learning machines: random neurons, random features and kernels. Cognit. Comput. 6, 376–390 (2014). doi:10.1007/s12559-014-9255-2
Acknowledgements
The authors wish to thank Universiti Teknologi Malaysia (UTM) under Research University Grant Vot-02G31 & Vot-15H17 and Ministry of Higher Education Malaysia (MOHE) under the Fundamental Research Grant Scheme (FRGS Vot-4F551) for the completion of the research.
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Alade, O.A., Selamat, A., Sallehuddin, R. (2018). A Review of Advances in Extreme Learning Machine Techniques and Its Applications. In: Saeed, F., Gazem, N., Patnaik, S., Saed Balaid, A., Mohammed, F. (eds) Recent Trends in Information and Communication Technology. IRICT 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-59427-9_91
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