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Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain
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applsci-11-00796.pdf
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1.72 MB
Format
Adobe PDF
Checksum (MD5)
b41bba375425b196bef306ef79dfc4ea
Author(s)
Althnian, Alhanoof
AlSaeed, Duaa
Al-Baity, Heyam
Samha, Amani
Dris, Alanoud Bin
Alzakari, Najla
Abou Elwafa, Afnan
Kurdi, Heba
Date Issued
January 15, 2021
Publisher
Multidisciplinary Digital Publishing Institute
Citation
Applied Sciences 11 (2): 796 (2021)
Version
Final published version
Abstract
Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.
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Creative Commons Attribution
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DOI of Published Version
http://dx.doi.org/10.3390/app11020796
