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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universitas Indonesia
2019-07-01
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| Series: | International Journal of Technology |
| Subjects: | |
| Online Access: | http://ijtech.eng.ui.ac.id/article/view/1743 |
| Summary: | 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 proposed. The ML-ESB algorithm is a modified SMOTEBagging technique
in which the ensemble of multiple instances of the single learner is replaced
by multiple distinct classifiers. The proposed ML-ESB is designed for handling
only the binary class imbalance problem. In ML-ESB the ensembles of multiple
distinct classifiers include Naïve Bays, Support Vector Machine, Logistic Regression
and Decision Tree (C4.5) is used. The performance of ML-ESB is evaluated based
on six binary imbalanced benchmark datasets using evaluation measures such as
specificity, sensitivity, and area under receiver operating curve. The obtained
results are compared with those of SMOTEBagging, SMOTEBoost, and cost-sensitive
MCS algorithms with different imbalance ratios (IR). The ML-ESB algorithm
outperformed other existing methods on four datasets with high dimensions and
class IR, whereas it showed moderate performance on the remaining two low
dimensions and small IR value datasets. |
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| ISSN: | 2086-9614 2087-2100 |