Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task

Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. Notably, Kernel Extreme Learning Machine (KELM), as a fast and effective classification method, has received widespread attention. However, traditional KELM algorithms have some problems when dealing with large-scale data, such as the need to adjust hyperparameters, poor interpretability, and low classification accuracy. To address these problems, this paper proposes an Enhanced Adaptive Whale Optimization Algorithm to optimize Kernel Extreme Learning Machine (EAWOA-KELM). Various methods were used to improve WOA. As a first step, a novel adaptive perturbation technique employing T-distribution is proposed to perturb the optimal position and avoid being trapped in a local maximum. Secondly, the WOA's position update formula was modified by incorporating inertia weight ω and enhancing convergence factor α, thus improving its capability for local search. Furthermore, inspired by the grey wolf optimization algorithm, use 3 excellent particle surround strategies instead of the original random selecting particles. Finally, a novel Levy flight was implemented to promote the diversity of whale distribution. Results from experiments confirm that the enhanced WOA algorithm outperforms the standard WOA algorithm in terms of both fitness value and convergence speed. EAWOA demonstrates superior optimization accuracy compared to WOA across 21 test functions, with a notable edge on certain functions. The application of the upgraded WOA algorithm in KELM significantly improves the accuracy and efficiency of data classification by optimizing hyperparameters. This paper selects 7 datasets for classification experiments. Compared with the KELM optimized by WOA, the EAWOA optimized KELM in this paper has a significant improvement in performance, with a 5%-6% lead on some datasets, indicating the effectiveness of EAWOA-KELM in classification tasks.