Comparative Evaluation of Support Vector Machine and Random Forest Machine Learning Algorithms
Effects of Training Set Size
Abstract
This study examined the performance of Support Vector Machine (SVM) and Random Forest (RF) algorithms using an Object-Based Image Analysis (OBIA) model in the metropolitan area of Barranquilla, Colombia. The purpose was to investigate how changes in training set size and imbalance in land cover classes influence the accuracy of classifier models. Kappa coefficient values and overall accuracy consistently revealed that SVM outperformed RF. Additionally, the inability to calibrate certain SVM parameters in arcgis Pro posed challenges. The choice of the number of trees in RF proved to be crucial, with a limited number of trees (50) affecting the model’s adaptability, especially in imbalanced datasets. This study highlights the complexity of choosing and configuring machine learning models, emphasizing the importance of carefully considering class proportions and homogeneity in data distributions to achieve accurate predictions in land use and land cover classification.According to the findings, achieving user accuracies exceeding 90% in clean grass, forests, road networks, and continental water classes, using the SVM model in arcgis Pro, requires assigning training samples covering 2%, 1%, 3%, and 8% of the classified area, respectively.
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