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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Jing-Yee Ong
Multimedia University
Computer Science & IT

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Addressing overfitting in comparative study for deep learning-based classification Jing-Yee Ong; Lee-Yeng Ong; Meng-Chew Leow
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 23, No 3: June 2025
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v23i3.26451

Abstract

Despite significant advancements in deep learning methodologies for animal species classification, there remains a notable research gap in effectively addressing biases inherent in training datasets, combating overfitting during model training, and enhancing overall performance to ensure reliable and accurate classification results in real-world applications. Therefore, this study explores the complex challenges of dog species classification, with a specific focus on addressing biases, combatting overfitting, and enhancing overall performance using deep learning methodologies. Initially, the Stanford Dog dataset serves as the foundation for training, complemented by additional data from annotated datasets. The primary aim is to mitigate biases and reduce overfitting, which is essential for improving the performance of deep learning-based classification in terms of dataset size and computational time. Feature extraction and few-shot learning techniques are compared to assess and improve the model performance. The experimentation involves the utilization of optimal classifiers, specifically InceptionV3 and Xception. In order to tackle overfitting, a range of strategies are deployed, including data augmentation, early stopping, and the integration of dropout and freezing layers which particularly achieved a better performance with Xception on the augmented dataset.
Co-Authors Lee-Yeng Ong Meng-Chew Leow