TY - JOUR
T1 - Hyper-Parameter Optimization of Semi-Supervised GANs Based-Sine Cosine Algorithm for Multimedia Datasets
AU - Al-Ragehi, Anas
AU - Abdulkadir, Said Jadid
AU - Muneer, Amgad
AU - Sadeq, Safwan
AU - Al-Tashi, Qasem
N1 - Funding Information:
Funding Statement: This research was supported by Universiti Teknologi PETRONAS, under the Yayasan Universiti Teknologi PETRONAS (YUTP) Fundamental Research Grant Scheme (YUTPFRG/015LC0-308).
Publisher Copyright:
© 2022 Tech Science Press. All rights reserved.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Generative adversarial networks
KW - grid search
KW - principal component analysis
KW - semi-supervised generative adversarial network
KW - sine-cosine algorithm
KW - SMOTE
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U2 - 10.32604/cmc.2022.027885
DO - 10.32604/cmc.2022.027885
M3 - Article
AN - SCOPUS:85130104246
SN - 1546-2218
VL - 73
SP - 2169
EP - 2186
JO - Computers, Materials and Continua
JF - Computers, Materials and Continua
IS - 1
ER -