Evaluating machine learning algorithms: The role of sample size and quality
TL;DR
SVM was the most consistent and reliable method across the tested scenarios, achieving high classification accuracy over a wide range of training sample sizes and maintaining strong robustness under noisy labels, which makes it a strong baseline choice when reference data are limited or imperfect.
Abstract
Abstract This study evaluates the performance of selected machine learning methods, Maximum Likelihood (MLC), Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Artificial Neural Networks (ANN), for land use/land cover (LULC) classification using Sentinel-2 satellite imagery. Each algorithm was tested across multiple classification scenarios that systematically varied both training sample size and training sample quality. To emulate realistic imperfections in reference data, controlled levels of label noise were introduced into the training set, and the resulting changes in classification performance were analysed. In addition, each model’s susceptibility to overfitting was assessed by comparing performance on training data and independent test data. To reduce the influence of a single random draw of training and testing pixels, all sampling-based experiments were repeated 10 times using different random seed values, and the reported results were summarized using repeated-run statistics. This design enabled a comparable assessment of how classification accuracy, stability, and generalization depend on dataset size and quality. The results indicate that SVM was the most consistent and reliable method across the tested scenarios, achieving high classification accuracy over a wide range of training sample sizes and maintaining strong robustness under noisy labels. Compared with the other methods, SVM showed lower sensitivity to degraded training data and a smaller tendency to overfit, which makes it a strong baseline choice when reference data are limited or imperfect.