A COMPARATIVE APPROACH TO SVM KERNEL FUNCTIONS VIA ACCURATE EVALUATING ALGORITHMS

Abstract

The Accurate Evaluation Algorithm in R Programming is used to assess the feasibility of classification using the SVM Radial Basis Function (RBF) kernel method. This algorithm encompasses some statistical tools. Among the various scenarios for improving dataset accuracy, employing the accurate evaluating algorithm before classifying with the SVM RBF kernel method is crucial. The current objective is to achieve an enhanced accuracy of up to 83% by leveraging this algorithm. The significance of this research lies in the development of an accurate evaluation algorithm for data analysis and its application using the SVM RBF kernel method. This contribution provides a solution for players in the digital insurance business within the data processing industry. The novelty lies in the accurate evaluating algorithm, which is thoroughly explained alongside simulation graphs for sum insured data. The accuracy comparisons based on the root-mean-square error (RMSE) and density are extensively investigated. The results conclude that employing this algorithm leads to more accurate data analysis (83% accuracy).