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

¹Vocational Training Center, FoShan Open University, FoShan, Guangdong Province, China.

|January 3, 2025

Summary

This study introduces an Enhanced Adaptive Whale Optimization Algorithm (EAWOA) to improve Kernel Extreme Learning Machine (KELM) for data classification. EAWOA enhances KELM

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Area of Science:

Machine Learning
Data Mining
Optimization Algorithms

Background:

Kernel Extreme Learning Machine (KELM) is a popular classification method.
Traditional KELM faces challenges with large datasets, including hyperparameter tuning and accuracy.
Existing optimization algorithms may struggle with local maxima and convergence speed.

Purpose of the Study:

To propose an Enhanced Adaptive Whale Optimization Algorithm (EAWOA) for optimizing Kernel Extreme Learning Machine (KELM).
To address limitations of traditional KELM, such as hyperparameter dependency and suboptimal classification accuracy.
To improve the efficiency and performance of data classification using an optimized KELM model.

Main Methods:

Developed an Enhanced Adaptive Whale Optimization Algorithm (EAWOA) by incorporating T-distribution perturbation, modified position updates with inertia weight (ω) and convergence factor (α), grey wolf-inspired particle strategies, and Levy flight.
Applied EAWOA to optimize the hyperparameters of the Kernel Extreme Learning Machine (KELM) for data classification tasks.
Evaluated EAWOA against standard Whale Optimization Algorithm (WOA) on 21 test functions and 7 benchmark datasets.

Main Results:

EAWOA demonstrated superior optimization accuracy and faster convergence compared to the standard WOA across various test functions.
The EAWOA-optimized KELM achieved a 5%-6% performance improvement on certain datasets compared to WOA-optimized KELM.
Optimizing KELM hyperparameters with EAWOA significantly enhanced data classification accuracy and efficiency.

Conclusions:

The proposed EAWOA is an effective optimization algorithm, outperforming standard WOA in terms of accuracy and convergence speed.
EAWOA-KELM offers a significant improvement for data classification tasks, particularly for large-scale datasets.
This enhanced approach provides a more robust and accurate solution for machine learning classification problems.