Review of Optimization in Improving Extreme Learning Machine

• Main Authors: Nilesh Rathod, Sunil Wankhade
• Format: Article
• Language: English
• Published: European Alliance for Innovation (EAI) 2021-09-01
• Series: EAI Endorsed Transactions on Industrial Networks and Intelligent Systems
• Subjects: extreme learning machine (elm); single-feedforward neural networks; kernel functions; sensitivity; input weights and activation bias
• Online Access: https://eudl.eu/pdf/10.4108/eai.17-9-2021.170960

Now a days Extreme Learning Machine has gained a lot of interest because of its noteworthy qualities over single hidden-layer feedforward neural networks and the kernel functions. Even if ELM has many advantages, it has some potential shortcomings such as performance sensitivity to the underlying state of the hidden neurons, input weights and the choice of functions of activation. To overcome the limitations of traditional ELM, analysts have devised numerical methods to optimise specific parts of ELM in order to enhance ELM performance for a variety of complicated difficulties and applications. Hence through this study, we intend to study the different algorithms developed for optimizing the ELM to enhance its performance in the aspects of survey criteria such as datasets, algorithm, objectives, training time, accuracy, error rate and the hidden neurons. This study will help other researchers to find out the research issues that lowering the performance of the ELM.