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A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem

A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem

Debashree Devi, Suyel Namasudra, Seifedine Kadry
Copyright: © 2020 |Volume: 16 |Issue: 3 |Pages: 27
ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799804994|DOI: 10.4018/IJDWM.2020070104
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MLA

Devi, Debashree, et al. "A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem." IJDWM vol.16, no.3 2020: pp.60-86. http://doi.org/10.4018/IJDWM.2020070104

APA

Devi, D., Namasudra, S., & Kadry, S. (2020). A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem. International Journal of Data Warehousing and Mining (IJDWM), 16(3), 60-86. http://doi.org/10.4018/IJDWM.2020070104

Chicago

Devi, Debashree, Suyel Namasudra, and Seifedine Kadry. "A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem," International Journal of Data Warehousing and Mining (IJDWM) 16, no.3: 60-86. http://doi.org/10.4018/IJDWM.2020070104

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Abstract

The subject of a class imbalance is a well-investigated topic which addresses performance degradation of standard learning models due to uneven distribution of classes in a dataspace. Cluster-based undersampling is a popular solution in the domain which offers to eliminate majority class instances from a definite number of clusters to balance the training data. However, distance-based elimination of instances often got affected by the underlying data distribution. Recently, ensemble learning techniques have emerged as effective solution due to its weighted learning principle of rare instances. In this article, a boosting aided adaptive cluster-based undersampling technique is proposed to facilitate elimination of learning- insignificant majority class instances from the clusters, detected through AdaBoost ensemble learning model. The proposed work is validated with seven existing cluster based undersampling techniques for six binary datasets and three classification models. The experimental results have established the effectives of the proposed technique than the existing methods.

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