Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

Distinct Multiple Learner-Based Ensemble SMOTEBagging (ML-ESB) Method for Classification of Binary Class Imbalance Problems

Traditional classification algorithms often fail in learning from highly imbalanced datasets because the training involves most of the samples from majority class compared to the other existing minority class. In this paper, a Multiple Learners-based Ensemble SMOTEBagging (ML-ESB) technique is p...

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Bibliographic Details
Main Authors: Dilip Singh Sisodia, Upasna Verma
Format: Article
Language:English
Published: Universitas Indonesia 2019-07-01
Series:International Journal of Technology
Subjects:
An ensemble of classifiers
Area under receiver operating curve
Classification
Class imbalance problem
Sensitivity
SMOTE
SMOTEBagging
SMOTEBoost
Specificity
Online Access:http://ijtech.eng.ui.ac.id/article/view/1743

Internet

http://ijtech.eng.ui.ac.id/article/view/1743

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