TELKOMNIKA Telecommunication, Computing, Electronics and Control
HAR-MI method for multi-class imbalanced datasets

TELKOMNIKA Telecommunication, Computing, Electronics and Control
HAR-MI method for multi-class imbalanced datasets

Classifier, Data diversity, Hybrid approach redefinition-multiclass imbalance, Multi-class imbalance

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-MIMethod, 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.

H. Hartono, Yeni Risyani, Erianto Ongko, Dahlan Abdullah

DOI: 10.12928/TELKOMNIKA.v18i2.14818

Universitas Ahmad Dahlan

April 2020

Sri Wahyuni

ISSN: 1693-6930

http://journal.uad.ac.id/index.php/TELKOMNIKA

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TELKOMNIKA Telecommunication, Computing, Electronics and Control