A Review of Advances in Extreme Learning Machine Techniques and Its Applications

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.

[1]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Fuzhen Zhuang,et al.  Learning deep representations via extreme learning machines , 2015, Neurocomputing.

[3]  Xia Liu,et al.  Is Extreme Learning Machine Feasible? A Theoretical Assessment (Part I) , 2015, IEEE Trans. Neural Networks Learn. Syst..

[4]  Wentao Mao,et al.  Online sequential classification of imbalanced data by combining extreme learning machine and improved SMOTE algorithm , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).

[5]  Guang-Bin Huang,et al.  A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics , 2016, Neural Networks.

[6]  Shahaboddin Shamshirband,et al.  Extreme learning machine assessment for estimating sediment transport in open channels , 2016, Engineering with Computers.

[7]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[8]  Guang-Bin Huang,et al.  Trends in extreme learning machines: A review , 2015, Neural Networks.

[9]  Mohammad Hamiruce Marhaban,et al.  FASTA-ELM: A fast adaptive shrinkage/thresholding algorithm for extreme learning machine and its application to gender recognition , 2017, Neurocomputing.

[10]  He Jiang,et al.  FP-ELM: An online sequential learning algorithm for dealing with concept drift , 2016, Neurocomputing.

[11]  Huchuan Lu,et al.  Saliency detection via extreme learning machine , 2016, Neurocomputing.

[12]  Shifei Ding,et al.  Extreme learning machine and its applications , 2013, Neural Computing and Applications.

[13]  Guang-Bin Huang,et al.  What are Extreme Learning Machines? Filling the Gap Between Frank Rosenblatt’s Dream and John von Neumann’s Puzzle , 2015, Cognitive Computation.

[14]  Hui Lin,et al.  Global and Local Features Based Classification for Bleed-Through Removal , 2016 .

[15]  Honey Badrzadeh,et al.  Hourly runoff forecasting for flood risk management: Application of various computational intelligence models , 2015 .

[16]  Yanika Kongsorot,et al.  Improved convex incremental extreme learning machine based on ridgelet and PSO algorithm , 2016, 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE).

[17]  Shuai Li,et al.  Inverse-Free Extreme Learning Machine With Optimal Information Updating , 2016, IEEE Transactions on Cybernetics.

[18]  Guang-Bin Huang,et al.  Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[19]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.

[20]  Basant Yadav,et al.  Discharge forecasting using an Online Sequential Extreme Learning Machine (OS-ELM) model: A case study in Neckar River, Germany , 2016 .

[21]  Yevgeniy Bodyanskiy,et al.  Fast learning algorithm for deep evolving GMDH-SVM neural network in data stream mining tasks , 2016, 2016 IEEE First International Conference on Data Stream Mining & Processing (DSMP).

[22]  Nan Liu,et al.  Voting based extreme learning machine , 2012, Inf. Sci..

[23]  S. Balasundaram,et al.  Knowledge-based extreme learning machines , 2015, Neural Computing and Applications.

[24]  Davide Anguita,et al.  Statistical Learning Theory and ELM for Big Social Data Analysis , 2016, IEEE Computational Intelligence Magazine.

[25]  Lei Wang,et al.  Multiple kernel extreme learning machine , 2015, Neurocomputing.

[26]  Jing Zhang,et al.  Local extreme learning machine: local classification model for shape feature extraction , 2015, Neural Computing and Applications.

[27]  Alexandros Iosifidis,et al.  Graph Embedded Extreme Learning Machine , 2016, IEEE Transactions on Cybernetics.

[28]  Witold Pedrycz,et al.  An overview on the roles of fuzzy set techniques in big data processing: Trends, challenges and opportunities , 2017, Knowl. Based Syst..

[29]  Yimin Yang,et al.  Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification , 2016, IEEE Transactions on Cybernetics.

[30]  R. Deo,et al.  Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq , 2016 .

[31]  Zhongzhi Shi,et al.  Incremental extreme learning machine based on deep feature embedded , 2016, Int. J. Mach. Learn. Cybern..

[32]  Elizabeta Lazarevska.  Wind speed prediction with extreme learning machine , 2016, 2016 IEEE 8th International Conference on Intelligent Systems (IS).