Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Image Classification Using Deep and Classical Machine Learning on Small Datasets: A Complete Comparative

Version 1 : Received: 21 January 2022 / Approved: 25 January 2022 / Online: 25 January 2022 (08:24:17 CET)

How to cite: Miranda, G.; Rubio-Manzano, C. Image Classification Using Deep and Classical Machine Learning on Small Datasets: A Complete Comparative. Preprints 2022, 2022010367 (doi: 10.20944/preprints202201.0367.v1). Miranda, G.; Rubio-Manzano, C. Image Classification Using Deep and Classical Machine Learning on Small Datasets: A Complete Comparative. Preprints 2022, 2022010367 (doi: 10.20944/preprints202201.0367.v1).

Abstract

One of the most important challenges in the Machine and Deep Learning areas today is to build good models using small datasets, because sometimes it is not possible to have large ones. Several techniques have been proposed in the literature to address this challenge. This paper aims at studying the different available Deep Learning techniques and performing a thorough experimentation to analyze which technique or combination thereof improves the performance and effectiveness of the models. A complete comparison with classical Machine Learning techniques was carried out, to contrast the results obtained using both techniques when working with small datasets. Thirteen algorithms were implemented and trained using three different small datasets (MNIST, Fashion MNIST, and CIFAR-10). Each experiment was evaluated using a well-established set of metrics (Accuracy, Precision, Recall, F1, and the Matthews correlation coefficient). The experimentation allowed concluding that it is possible to find a technique or combination of them to mitigate a lack of data, but this depends on the nature of the dataset, the amount of data, and the metrics used to evaluate them.

Keywords

Artificial Intelligence; Deep Learning; Image Classification; Machine Learning; Predictive Models; Small Datasets; Supervised Learning

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our diversity statement.

Leave a public comment
Send a private comment to the author(s)
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.