Comparisons of SVM Kernels for Insurance Data Clustering
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 S...
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| Format: | Article |
| Language: | English |
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Ital Publication
2022-05-01
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| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://www.ijournalse.org/index.php/ESJ/article/view/1121 |
| _version_ | 1811241657271582720 |
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| author | Irfan Nurhidayat Busayamas Pimpunchat Samad Noeiaghdam Unai Fernández-Gámiz |
| author_facet | Irfan Nurhidayat Busayamas Pimpunchat Samad Noeiaghdam Unai Fernández-Gámiz |
| author_sort | Irfan Nurhidayat |
| collection | DOAJ |
| description | 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.
Doi: 10.28991/ESJ-2022-06-04-014
Full Text: PDF |
| first_indexed | 2024-04-12T13:39:18Z |
| format | Article |
| id | doaj.art-95b30f3b43094d6688383f04b6fbd1f5 |
| institution | Directory Open Access Journal |
| issn | 2610-9182 |
| language | English |
| last_indexed | 2024-04-12T13:39:18Z |
| publishDate | 2022-05-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj.art-95b30f3b43094d6688383f04b6fbd1f52022-12-22T03:30:53ZengItal PublicationEmerging Science Journal2610-91822022-05-016486688010.28991/ESJ-2022-06-04-014332Comparisons of SVM Kernels for Insurance Data ClusteringIrfan Nurhidayat0Busayamas Pimpunchat1Samad Noeiaghdam2Unai Fernández-Gámiz3Department of Mathematics, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520,Department of Mathematics, School of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, 10520,2) Industrial Mathematics Laboratory, Baikal School of BRICS, Irkutsk National Research Technical University, Irkutsk, 664074, Russia. 3) Department of Applied Mathematics and Programming, South Ural State University, Lenin Prospect 76, Chelyabinsk, 454080,Nuclear Engineering and Fluid Mechanics Department, University of the Basque Country UPV/EHU, Nieves Cano 12, Vitoria-Gasteiz, 01006,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. Doi: 10.28991/ESJ-2022-06-04-014 Full Text: PDFhttps://www.ijournalse.org/index.php/ESJ/article/view/1121insurance data clusteringsupport vector machinermseaucsum insured. |
| spellingShingle | Irfan Nurhidayat Busayamas Pimpunchat Samad Noeiaghdam Unai Fernández-Gámiz Comparisons of SVM Kernels for Insurance Data Clustering Emerging Science Journal insurance data clustering support vector machine rmse auc sum insured. |
| title | Comparisons of SVM Kernels for Insurance Data Clustering |
| title_full | Comparisons of SVM Kernels for Insurance Data Clustering |
| title_fullStr | Comparisons of SVM Kernels for Insurance Data Clustering |
| title_full_unstemmed | Comparisons of SVM Kernels for Insurance Data Clustering |
| title_short | Comparisons of SVM Kernels for Insurance Data Clustering |
| title_sort | comparisons of svm kernels for insurance data clustering |
| topic | insurance data clustering support vector machine rmse auc sum insured. |
| url | https://www.ijournalse.org/index.php/ESJ/article/view/1121 |
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