Multilayer extreme learning machine: a systematic review

Majority of the learning algorithms used for the training of feedforward neural networks (FNNs), such as backpropagation (BP), conjugate gradient method, etc. rely on the traditional gradient method. Such algorithms have a few drawbacks, including slow convergence, sensitivity to noisy data, local minimum problem, etc. One of the alternatives to overcome such issues is Extreme Learning Machine (ELM), which requires less training time, ensures global optimum and enhanced generalization in neural networks. ELM has a single hidden layer, which poses memory constraints in some problem domains. An extension to ELM, Multilayer ELM (ML-ELM) performs unsupervised learning by utilizing ELM autoencoders and eliminates the need of parameter tuning, enabling better representation learning as it consists of multiple layers. This paper provides a thorough review of ML-ELM architecture development and its variants and applications. The state-of-the-art comparative analysis between ML-ELM and other machine and deep learning classifiers demonstrate the efficacy of ML-ELM in the niche domains of Computer Science which further justifies its competency and effectiveness.