ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM
Ensemble learning by combining several single or another ensemble classifier is one of the procedures to solve the imbalance problem in multiclass data. However, this approach is still facing the question of how the ensemble methods obtain their higher performance. In this paper, the investigation...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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UUM Press
2021-02-01
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| Series: | Journal of ICT |
| Subjects: | |
| Online Access: | https://e-journal.uum.edu.my/index.php/jict/article/view/13148 |
| _version_ | 1818490178020311040 |
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| author | Mohd Shamrie Sainin Rayner Alfred Faudziah Ahmad |
| author_facet | Mohd Shamrie Sainin Rayner Alfred Faudziah Ahmad |
| author_sort | Mohd Shamrie Sainin |
| collection | DOAJ |
| description |
Ensemble learning by combining several single or another ensemble classifier is one of the procedures to solve the imbalance problem in multiclass data. However, this approach is still facing the question of how the ensemble methods obtain their higher performance. In this paper, the investigation is carried out on the design of the ensemble meta classifier with sampling and feature selection for imbalance multiclass data. The specific objectives are 1) to improve the ensemble classifier through data-level approach (sampling and feature selection); 2) to perform experiments on sampling, feature selection, and ensemble classifier model; and 3) to evaluate the performance of the ensemble classifier. To fulfill the objectives, a preliminary data collection of Malaysian plants leaf images was prepared, experimented, and comparing the results. The ensemble design is also tested with another three high imbalance ratio benchmark data. It is found that the design using sampling, feature selection and ensemble classifier method using AdaboostM1 with Random Forest (also an ensemble classifier) provides the improved performance throughout the investigation. The result of this study is important to the ongoing problem of multiclass imbalance where specific structure and its performance can be improved in terms of processing time and accuracy.
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| first_indexed | 2024-12-10T17:13:39Z |
| format | Article |
| id | doaj.art-2d43f60c0bfb401b9b9a6ada4ab90aae |
| institution | Directory Open Access Journal |
| issn | 1675-414X 2180-3862 |
| language | English |
| last_indexed | 2024-12-10T17:13:39Z |
| publishDate | 2021-02-01 |
| publisher | UUM Press |
| record_format | Article |
| series | Journal of ICT |
| spelling | doaj.art-2d43f60c0bfb401b9b9a6ada4ab90aae2022-12-22T01:40:11ZengUUM PressJournal of ICT1675-414X2180-38622021-02-01202ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEMMohd Shamrie Sainin0Rayner Alfred1Faudziah Ahmad2Faculty of Computing and Informatics, Universiti Malaysia Sabah, MalaysiaFaculty of Computing and Informatics, Universiti Malaysia Sabah, MalaysiaSchool of Computing, Universiti Utara Malaysia, Malaysia Ensemble learning by combining several single or another ensemble classifier is one of the procedures to solve the imbalance problem in multiclass data. However, this approach is still facing the question of how the ensemble methods obtain their higher performance. In this paper, the investigation is carried out on the design of the ensemble meta classifier with sampling and feature selection for imbalance multiclass data. The specific objectives are 1) to improve the ensemble classifier through data-level approach (sampling and feature selection); 2) to perform experiments on sampling, feature selection, and ensemble classifier model; and 3) to evaluate the performance of the ensemble classifier. To fulfill the objectives, a preliminary data collection of Malaysian plants leaf images was prepared, experimented, and comparing the results. The ensemble design is also tested with another three high imbalance ratio benchmark data. It is found that the design using sampling, feature selection and ensemble classifier method using AdaboostM1 with Random Forest (also an ensemble classifier) provides the improved performance throughout the investigation. The result of this study is important to the ongoing problem of multiclass imbalance where specific structure and its performance can be improved in terms of processing time and accuracy. https://e-journal.uum.edu.my/index.php/jict/article/view/13148Imbalance, multiclass, ensemble, feature selection, sampling |
| spellingShingle | Mohd Shamrie Sainin Rayner Alfred Faudziah Ahmad ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM Journal of ICT Imbalance, multiclass, ensemble, feature selection, sampling |
| title | ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM |
| title_full | ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM |
| title_fullStr | ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM |
| title_full_unstemmed | ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM |
| title_short | ENSEMBLE META CLASSIFIER WITH SAMPLING AND FEATURE SELECTION FOR DATA WITH IMBALANCE MULTICLASS PROBLEM |
| title_sort | ensemble meta classifier with sampling and feature selection for data with imbalance multiclass problem |
| topic | Imbalance, multiclass, ensemble, feature selection, sampling |
| url | https://e-journal.uum.edu.my/index.php/jict/article/view/13148 |
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