Comparative Investigation of Learning Algorithms for Image Classification with Small Dataset. Issue 10 (24th August 2021)

• Record Type: Journal Article
• Authors: Iqbal, Imran; Odesanmi, Gbenga Abiodun; Wang, Jianxiang; Liu, Li
• Is Part Of: Applied artificial intelligence. Volume 35:Issue 10(2021)
• Page Start: 697
• Page End: 716
• Publication Date: 2021-08-24
• Subjects: Artificial intelligence -- Periodicals; 006.3
• DOI: 10.1080/08839514.2021.1922841
• Languages: English
• ISSNs: 0883-9514

Abstract

ABSTRACT: Increase in popularity of deep learning in various research areas leads to use it in resolving image classification problems. The objective of this research is to compare and to find learning algorithms which perform better for image classification task with small dataset. We have also tuned the hyperparameters associated with optimizers and models to improve performance. First, we performed several experiments using eight learning algorithms to come closer to optimal values of hyperparameters. Then, we executed twenty-four final experiments with near optimum values of hyperparameters to find the best learning algorithm. Experimental results showed that the AdaGrad learning algorithm achieves better accuracy, lesser training time, as well as fewer memory utilization compared to the rest of the learning algorithms.