Wavelet extreme learning machine and deep learning for data classification

Recently, the Extreme Learning Machine (ELM) algorithm has been applied to various fields due to its rapidity and significant generalization performance. Traditionally, deep learning (DL) and wavelet neural networks (WNN) methods reach a high classification accuracy in machine learning applications. As a result, a new structure based on WNN, deep architecture and ELM is proposed in this paper. The proposed method is based on Extreme Learning Machine Auto-Encoder with DL structure and a composite wavelet activation function used in the hidden nodes. To evaluate the performance of our approach, we used standard benchmark data-sets, namely COIL-20, Pima Indian Diabetes (PID), MNIST and EMNIST. Experimental results show that our method offers satisfactory results and performance compared to other approaches. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a new structure based on WNN, deep architecture, and ELM, which improves the classification accuracy in machine learning applications by using a composite wavelet activation function and an ELM auto-encoder with DL structure.