HAR-MI method for multi-class imbalanced datasets

H. Hartono, Yeni Risyani, Erianto Ongko, Dahlan Abdullah
<span title="2020-04-01">2020</span> <i title="Universitas Ahmad Dahlan"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/avuzjspx3nh5lboz3nsmpd3ba4" style="color: black;">TELKOMNIKA (Telecommunication Computing Electronics and Control)</a> </i> &nbsp;
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
more &raquo; ... reprocessing 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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