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. Preprints2022, 2022010367. https://doi.org/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. https://doi.org/10.20944/preprints202201.0367.v1
Miranda, G.; Rubio-Manzano, C. Image Classification Using Deep and Classical Machine Learning on Small Datasets: A Complete Comparative. Preprints2022, 2022010367. https://doi.org/10.20944/preprints202201.0367.v1
APA Style
Miranda, G., & Rubio-Manzano, C. (2022). Image Classification Using Deep and Classical Machine Learning on Small Datasets: A Complete Comparative. Preprints. https://doi.org/10.20944/preprints202201.0367.v1
Chicago/Turabian Style
Miranda, G. and Clemente Rubio-Manzano. 2022 "Image Classification Using Deep and Classical Machine Learning on Small Datasets: A Complete Comparative" Preprints. https://doi.org/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
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
