Adaptive Particle Swarm Optimization Applied in Conjunction with Support Vector Machine

This study showcases the outcomes of implementing the Support Vector Machine (SVM) algorithm with an adaptive particle swarm optimization (APSO) technique. The researchers primarily focused on examining the performance of the linear kernel SVM and found that APSO was able to identify the optimal value for the involved constant. In order to test the effectiveness of the proposed approach, two commonly used optimization techniques were employed—the Grid search algorithm and the typical Particle Swarm Optimization (PSO). These methods were applied to a breast cancer classification task to provide choices and help validate the concept. After conducting the experiments, it was found that by combining the support vector machine (SVM) with the adaptive PSO, satisfactory results can be obtained. The results obtained were conclusive, demonstrating that this method is an effective and promising approach for breast cancer classification. The findings suggest that the proposed approach has the potential to provide accurate and reliable predictions for the classification of breast cancer. By utilizing a combination of the adaptive particle swarm optimization (APSO) technique, it is possible to enhance the performance of the SVM algorithm and improve the accuracy of the classification model.