Evaluación comparativa de los algoritmos de aprendizaje automático Support Vector Machine y Random Forest
efectos del tamaño del conjunto de entrenamiento
Resumen
En el presente estudio se examinó el rendimiento de los algoritmos Support Vector Machine (SVM) y Random Forest (RF) utilizando un modelo de segmentación de imágenes basado en objetos (OBIA) en la zona metropolitana de Barranquilla, Colombia. El propósito fue investigar de qué manera los cambios en el tamaño de los conjuntos de entrenamiento y el desequilibrio en las clases de cobertura terrestre influyen en la precisión de los modelos clasificadores. Los valores del coeficiente Kappa y la precisión general revelaron que svm superó consistentemente a RF. Además, la imposibilidad de calibrar ciertos parámetros de SVM en ArcGIS Pro planteó desafíos. La elección del número de árboles en RF mostró ser fundamental, con un número limitado de árboles (50) que afectó la adaptabilidad del modelo, especialmente en conjuntos de datos desequilibrados. Este estudio resalta la complejidad de elegir y configurar modelos de aprendizaje automático, que acentúan la importancia de considerar cuidadosamente las proporciones de clases y la homogeneidad en las distribuciones de datos para lograr predicciones precisas en la clasificación de uso del suelo y cobertura terrestre. Según los hallazgos, alcanzar precisiones de usuario superiores al 90 % en las clases de pastos limpios, bosques, red vial y agua continental, mediante el modelo svm en ArcGIS Pro, requiere asignar muestras de entrenamiento que cubran respectivamente el 2 %, 1 %, 3 % y 8 % del área clasificada.
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