GDL

Journal from gdlhub / 2021-01-20 15:18:14
Oleh : H. Hartono, Yeni Risyani, Erianto Ongko, Dahlan Abdullah, Telkomnika
Dibuat : 2021-01-11, dengan 1 file

Keyword : classifier; data diversity; hybrid approach redefinition-multiclass imbalance; multi-class imbalance;
Url : http://journal.uad.ac.id/index.php/TELKOMNIKA/article/view/14818
Sumber pengambilan dokumen : web

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.

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