HAR-MI method for multi-class imbalanced datasets
| Main Authors: | Hartono, H.; STMIK IBBI, Risyani, Yeni; STMIK IBBI, Ongko, Erianto; Akademi Teknologi Industri Immanuel, Abdullah, Dahlan; Universitas Malikussaleh |
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| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Universitas Ahmad Dahlan
, 2020
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| Subjects: | |
| Online Access: |
http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/14818 http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/14818/7891 |
Daftar Isi:
- Research on multi-class imbalance from a number of researchers faces obstacles in the form of poor data diversity and a large number of classifiers. The Hybrid Approach Redefinition-Multiclass Imbalance (HAR-MI) method is a Hybrid Ensembles method which is the development of the Hybrid Approach Redefinion (HAR) method. This study has compared the results obtained with the Dynamic Ensemble Selection-Multiclass Imbalance (DES-MI) method in handling multiclass imbalance. In the HAR-MI Method, the preprocessing stage was carried out using the random balance ensembles method and dynamic ensemble selection to produce a candidate ensemble and the processing stages was carried out using different contribution sampling and dynamic ensemble selection to produce a candidate ensemble. This research has been conducted by using multi-class imbalance datasets sourced from the KEEL Repository. The results show that the HAR-MI method can overcome multi-class imbalance with better data diversity, smaller number of classifiers, and better classifier performance compared to a DES-MI method. These results were tested with a Wilcoxon signed-rank statistical test which showed that the superiority of the HAR-MI method with respect to DES-MI method.