Article Per Year (5 Year)
p-Index From 2020 - 2025
0.718
P-Index
This Author published in this journals
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

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Bulletin of Electrical Engineering and Informatics

 1 
Hybrid approach redefinition-multi class with resampling and feature selection for multi-class imbalance with overlapping and noise Erianto Ongko; Hartono Hartono
Bulletin of Electrical Engineering and Informatics Vol 10, No 3: June 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i3.3057

Abstract

Class imbalance and overlapping on multi-class can reduce the performance and accuracy of the classification. Noise must also be considered because it can reduce the performance of classification. With a resampling algorithm and feature selection, this paper proposes a method for improving the performance of hybrid approach redefinition-multi class (HAR-MI). Resampling algorithm can overcome the problem of noise but cannot handle overlapping well. Feature selection is good at dealing with overlapping but can experience a decrease in quality if there is a noise. The HAR-MI approach is a way to deal with multi-class imbalance issues, but it has some drawbacks when dealing with overlapping. The contribution of this paper is to suggest a new approach for dealing with class imbalance, overlapping, and noise in multi-class. This is accomplished by employing minimizing overlapping selection (MOSS) as an ensemble learning algorithm and a preprocessing technique in HAR-MI, as well as employing multi-class combination cleaning and resampling (MC-CCR) as a resampling algorithm at the processing stage. When subjected to overlapping and classifier performance, it is discovered that the proposed method produces good results, as evidenced by higher augmented r-value, class average accuracy, class balance accuracy, multi class g-mean, and confusion entropy.
Co-Authors Dahlan Abdullah Hartono Hartono Yeni Risyani