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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics International Journal of Advances in Intelligent Informatics JTIK (Jurnal Teknik Informatika Kaputama)
Erianto Ongko
Akademi Teknologi Industri Immanuel
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Advances in Intelligent Informatics

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Hybrid approach redefinition with cluster-based instance selection in handling class imbalance problem Hartono Hartono; Erianto Ongko; Dahlan Abdullah
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.515

Abstract

Class Imbalance problems often occur in the classification process, the existence of these problems is characterized by the tendency of a class to have instances that are much larger than other classes. This problem certainly causes a tendency towards low accuracy in minority classes with smaller number of instances and also causes important information on minority classes not to be obtained. Various methods have been applied to overcome the problem of the imbalance class. One of them is the Hybrid Approach Redefinition method which is one of the Hybrid Ensembles methods. The tendency to pay attention to the performance classifier, has led to an understanding of the importance of selecting an instance that will be used as a classifier. In the classic Hybrid Approach Redefinition method classifier selection is done randomly using the Random Under Sampling approach, and it is interesting to study how performance is obtained if the sampling process is based on Cluster-Based by selecting existing instances. The purpose of this study is to apply the Hybrid Approach Redefinition method with Cluster-Based Instance Selection (CBIS) approach so that it can obtain a better performance classifier. The results showed that Hybrid Approach Redefinition with cluster-based instance selection gave better results on the number of classifiers, data diversity, and performance classifiers compared to classic Hybrid Approach Redefinition.
Co-Authors Dahlan Abdullah Hartono Hartono Yeni Risyani