Muhammad Izham Ismail
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Preferred name
Muhammad Izham Ismail
Official Name
Muhammad Izham, Ismail
Alternative Name
Ismail, Muhammad Izham
Ismail, M. I.
Main Affiliation
Scopus Author ID
57212349161
Researcher ID
GAQ-6580-2022

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  • Publication
    Analysis of the effectiveness of Metaheuristic methods on Bayesian optimization in the classification of visual field defects
    (MDPI, 2023)
    Masyitah Abu
    ;
    Nik Adilah Hanin Zahri
    ;
    Fumiyo Fukumoto
    ;
    Amiza Amir
    ;
    Muhammad Izham Ismail
    ;
    Yoshimi Suzuki
    ;
    Azhany Yaakub
    Bayesian optimization (BO) is commonly used to optimize the hyperparameters of transfer learning models to improve the model’s performance significantly. In BO, the acquisition functions direct the hyperparameter space exploration during the optimization. However, the computational cost of evaluating the acquisition function and updating the surrogate model can become prohibitively expensive due to increasing dimensionality, making it more challenging to achieve the global optimum, particularly in image classification tasks. Therefore, this study investigates and analyses the effect of incorporating metaheuristic methods into BO to improve the performance of acquisition functions in transfer learning. By incorporating four different metaheuristic methods, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) Optimization, Harris Hawks Optimization, and Sailfish Optimization (SFO), the performance of acquisition function, Expected Improvement (EI), was observed in the VGGNet models for visual field defect multi-class classification. Other than EI, comparative observations were also conducted using different acquisition functions, such as Probability Improvement (PI), Upper Confidence Bound (UCB), and Lower Confidence Bound (LCB). The analysis demonstrates that SFO significantly enhanced BO optimization by increasing mean accuracy by 9.6% for VGG-16 and 27.54% for VGG-19. As a result, the best validation accuracy obtained for VGG-16 and VGG-19 is 98.6% and 98.34%, respectively.
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