ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA
Imbalanced data is a commonly encountered issue in classification analysis. This issue gives rise to prediction errors in the classification process, which in turn affects the sensitivity, particularly in the minority class. Resampling techniques can be employed as a means to mitigate the issue of I...
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Universitas Pattimura
2024-03-01
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| Online Access: | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10285 |
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| author | Laila Qadrini Hikmah Hikmah Elviani Tande Ignasius Presda Aulia Atika Maghfirah Nilawati Nilawati Handayani Handayani |
| author_facet | Laila Qadrini Hikmah Hikmah Elviani Tande Ignasius Presda Aulia Atika Maghfirah Nilawati Nilawati Handayani Handayani |
| author_sort | Laila Qadrini |
| collection | DOAJ |
| description | Imbalanced data is a commonly encountered issue in classification analysis. This issue gives rise to prediction errors in the classification process, which in turn affects the sensitivity, particularly in the minority class. Resampling techniques can be employed as a means to mitigate the issue of Imbalanced data. Furthermore, ensemble approaches are Utilized in the classification procedure to augment the performance of classification. The present study assesses the efficacy of the bagging ensemble approach in conjunction with ADASYN as a means of addressing the aforementioned issue. The dataset Utilized in this work comprises Imbalanced Glass Identification data, Imbalanced Iris data, and Imbalanced synthetic data. The study Centres on the Utilization of Support Vector Machines (SVM) with parameter optimization using repeated cross-validation (k = 10) and the application of multinomial regression. The evaluation of classification outcomes involves a comparison between the ensemble technique and multinomial regression. This comparison is conducted under pre- and post-resampling conditions, with the evaluation metrics being accuracy, sensitivity, and specificity. The analysis of classification outcomes across the three datasets suggests that the ensemble resampling SVM approach and multinomial regression exhibit superior performance compared to the ensemble SVM and multinomial regression approaches when applied to non-resampled data. Resampling of data has been observed to enhance sensitivity, particularly in the minority class. |
| format | Article |
| id | doaj-art-e8af592a6f5140a4ad3e98ce6fdb96e0 |
| institution | Kabale University |
| issn | 1978-7227 2615-3017 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Universitas Pattimura |
| record_format | Article |
| series | Barekeng |
| spelling | doaj-art-e8af592a6f5140a4ad3e98ce6fdb96e02025-08-20T03:35:54ZengUniversitas PattimuraBarekeng1978-72272615-30172024-03-011810269028010.30598/barekengvol18iss1pp0269-028010285ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATALaila Qadrini0Hikmah Hikmah1Elviani Tande2Ignasius Presda3Aulia Atika Maghfirah4Nilawati Nilawati5Handayani Handayani6Statistics Department, Faculty of Mathematics and Natural Sciences, University of West Sulawesi, IndonesiaStatistics Department, Faculty of Mathematics and Natural Sciences, University of West Sulawesi, IndonesiaStatistics Department, Faculty of Mathematics and Natural Sciences, University of West Sulawesi, IndonesiaStatistics Department, Faculty of Mathematics and Natural Sciences, University of West Sulawesi, IndonesiaStatistics Department, Faculty of Mathematics and Natural Sciences, University of West Sulawesi, IndonesiaStatistics Department, Faculty of Mathematics and Natural Sciences, University of West Sulawesi, IndonesiaStatistics Department, Faculty of Mathematics and Natural Sciences, University of West Sulawesi, IndonesiaImbalanced data is a commonly encountered issue in classification analysis. This issue gives rise to prediction errors in the classification process, which in turn affects the sensitivity, particularly in the minority class. Resampling techniques can be employed as a means to mitigate the issue of Imbalanced data. Furthermore, ensemble approaches are Utilized in the classification procedure to augment the performance of classification. The present study assesses the efficacy of the bagging ensemble approach in conjunction with ADASYN as a means of addressing the aforementioned issue. The dataset Utilized in this work comprises Imbalanced Glass Identification data, Imbalanced Iris data, and Imbalanced synthetic data. The study Centres on the Utilization of Support Vector Machines (SVM) with parameter optimization using repeated cross-validation (k = 10) and the application of multinomial regression. The evaluation of classification outcomes involves a comparison between the ensemble technique and multinomial regression. This comparison is conducted under pre- and post-resampling conditions, with the evaluation metrics being accuracy, sensitivity, and specificity. The analysis of classification outcomes across the three datasets suggests that the ensemble resampling SVM approach and multinomial regression exhibit superior performance compared to the ensemble SVM and multinomial regression approaches when applied to non-resampled data. Resampling of data has been observed to enhance sensitivity, particularly in the minority class.https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10285baggingadasynsvmmultinomial regression |
| spellingShingle | Laila Qadrini Hikmah Hikmah Elviani Tande Ignasius Presda Aulia Atika Maghfirah Nilawati Nilawati Handayani Handayani ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA Barekeng bagging adasyn svm multinomial regression |
| title | ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA |
| title_full | ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA |
| title_fullStr | ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA |
| title_full_unstemmed | ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA |
| title_short | ENSEMBLE RESAMPLING SUPPORT VECTOR MACHINE, MULTINOMIAL REGRESSION TO MULTICLASS IMBALANCED DATA |
| title_sort | ensemble resampling support vector machine multinomial regression to multiclass imbalanced data |
| topic | bagging adasyn svm multinomial regression |
| url | https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/10285 |
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