Open Access

ARTICLE

Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine Algorithm for Multimedia Datasets

by Anas Al-Ragehi¹, Said Jadid Abdulkadir^1,2,*, Amgad Muneer^1,2, Safwan Sadeq³, Qasem Al-Tashi^4,5

1 Computer and Information Sciences Department, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia
2 Centre for Research in Data Science (CERDAS), Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Malaysia
3 Mechanical Engineering Department, Universiti Teknologi PETRONAS, Seri Iskandar, 32610, Perak, Malaysia
4 Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
5 University of Albaydha, Albaydha, Yemen

* Corresponding Author: Said Jadid Abdulkadir. Email:

Computers, Materials & Continua 2022, 73(1), 2169-2186. https://doi.org/10.32604/cmc.2022.027885

Received 27 January 2022; Accepted 30 March 2022; Issue published 18 May 2022

Abstract

Generative Adversarial Networks (GANs) are neural networks that allow models to learn deep representations without requiring a large amount of training data. Semi-Supervised GAN Classifiers are a recent innovation in GANs, where GANs are used to classify generated images into real and fake and multiple classes, similar to a general multi-class classifier. However, GANs have a sophisticated design that can be challenging to train. This is because obtaining the proper set of parameters for all models-generator, discriminator, and classifier is complex. As a result, training a single GAN model for different datasets may not produce satisfactory results. Therefore, this study proposes an SGAN model (Semi-Supervised GAN Classifier). First, a baseline model was constructed. The model was then enhanced by leveraging the Sine-Cosine Algorithm and Synthetic Minority Oversampling Technique (SMOTE). SMOTE was used to address class imbalances in the dataset, while Sine Cosine Algorithm (SCA) was used to optimize the weights of the classifier models. The optimal set of hyperparameters (learning rate and batch size) were obtained using grid manual search. Four well-known benchmark datasets and a set of evaluation measures were used to validate the proposed model. The proposed method was then compared against existing models, and the results on each dataset were recorded and demonstrated the effectiveness of the proposed model. The proposed model successfully showed improved test accuracy scores of 1%, 2%, 15%, and 5% on benchmarking multimedia datasets; Modified National Institute of Standards and Technology (MNIST) digits, Fashion MNIST, Pneumonia Chest X-ray, and Facial Emotion Detection Dataset, respectively.

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