Enhancing extreme learning machines using cross-entropy moth-flame optimization algorithm

• Main Authors: Alade, Oyekale Abel, Sallehuddin, Roselina, Mohamed Radzi, Nor Haizan
• Format: Article
• Published: Centre for Environment and Socio-Economic Research Publications 2022
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://eprints.utm.my/id/eprint/98680/; http://www.ceser.in/ceserp/index.php/ijai/article/view/6857
• Institution: Universiti Teknologi Malaysia

Extreme Learning Machines (ELM) learn fast and eliminate the tuning of input weights and biases. However, ELM does not guarantee the optimal setting of the weights and biases due to random input parameters initialization. Therefore, ELM suffers from instability of output, large network size, and deg...