Pimpunchat, Busayamas

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Pimpunchat, Busayamas
Alternative Name
Pimpunchat, B.
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Email
busayamas.pi@kmitl.ac.th

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    More accurate simulation for insurance data based on a modified SVM polynomial method
    (2023-06-01)
    Nurhidayat, Irfan
    ;
    Pimpunchat, Busayamas
    ;
    Klomsungcharoen, Wiriyabhorn
    This study aims to present the modified SVM polynomial method in order to evaluate insurance data. The research methodology discusses classical and modified SVM polynomial methods by R programming, and uses performance profiles to create the most preferable methods. It offers a new algorithm called an accurate evaluating algorithm as the way to construct the modified SVM polynomial method. The classical SVM polynomial method is also represented as the main idea in finding the modified polynomial SVM method. Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROCs) Curve, Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds are further named an accurate evaluating algorithm, employed to build the modified SVM polynomial method. The research paper also presents the best performance profiles based on the computing time and the number of iterations of both classical and modified SVM polynomial methods. Performance profiles show numerical comparisons based on both methods involving insurance data also displayed in this paper. It can be concluded that applying an accurate evaluating algorithm on the modified SVM polynomial method will improve the data accuracy up to 86% via computing time and iterations compared to the classical SVM polynomial method, which is only 79%. This accurate evaluating algorithm can be applied to various large-sized data by utilizing R programming with changing any suitable kernels for that data. This vital discovery will offer solutions for faster and more accurate data analysis that can benefit researchers, the private sector, or governments struggling with data.
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    Comparisons of SVM Kernels for Insurance Data Clustering
    (2022-08-01)
    Nurhidayat, Irfan
    ;
    Pimpunchat, Busayamas
    ;
    Noeiaghdam, Samad
    ;
    Fernández-Gámiz, Unai
    This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data.
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    A COMPARATIVE APPROACH TO SVM KERNEL FUNCTIONS VIA ACCURATE EVALUATING ALGORITHMS
    (2023-08-01)
    Nurhidayat, Irfan
    ;
    Pimpunchat, Busayamas
    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).