IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification

IRS-BAG-Integrated Radius-SMOTE Algorithm with Bagging Ensemble Learning Model for Imbalanced Data Set Classification

Imbalanced learning problems are a challenge faced by classifiers when data samples have an unbalanced distribution among classes. The Synthetic Minority Over-Sampling Technique (SMOTE) is one of the most well-known data pre-processing methods. Problems that arise when oversampling with SMOTE are th...

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Bibliographic Details
Main Authors: Lilis Yuningsih, Gede Angga Pradipta, Dadang Hermawan, Putu Desiana Wulaning Ayu, Dandy Pramana Hostiadi, Roy Rudolf Huizen
Format: Article
Language:English
Published: Ital Publication 2023-10-01
Series:Emerging Science Journal
Subjects:
imbalanced data
oversampling
smote
bagging
classification
machine learning.
Online Access:https://www.ijournalse.org/index.php/ESJ/article/view/1758

Internet

https://www.ijournalse.org/index.php/ESJ/article/view/1758

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