Improving ELM Model using DL Feature Extraction and GWO: Application to Image Classification

What is it about?

This study boosts ELM's image classification performance by jointly optimizing input features and initializing weights/biases. Hybrid deep feature extraction and metaheuristic optimization overcome the limitations of standard ELMs, achieving state-of-the-art accuracy, especially under noisy conditions.

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Why is it important?

This research is important because it unlocks the potential of ELMs for tackling challenging visual recognition problems through holistic optimization, while retaining their computational merits. The robustness achieved highlights their viability as efficient deep learning models.