Application Classification of Feature Selection RBF Kernel SVM in Different Datasets

This paper introduces a novel image classification method that integrates feature selection with a radial basis function (RBF) kernel support vector machine (SVM). Our approach utilizes a heuristic search algorithm for effective feature selection, which significantly reduces the dimensionality of the input data and enhances classification performance. The selected features are then employed as inputs for training and testing the RBF kernel SVM algorithm. We conducted comparative experiments across various datasets, and the results indicate that our proposed feature selection RBF-SVM method demonstrates outstanding performance in classification accuracy and efficiency across different contexts.