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DOI: 10.14569/IJACSA.2019.0100307
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Empirical Assessment of Ensemble based Approaches to Classify Imbalanced Data in Binary Classification

Author 1: Prabhjot Kaur
Author 2: Anjana Gosain

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 3, 2019.

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Abstract: Classifying imbalanced data with traditional classifiers is a huge challenge now-a-days. Imbalance data is a situation wherein the ratio of data within classes is not same. Many real life situations deal with such problems e.g. Web spam detection, Credit card frauds, and Fraudulent telephone calls. The problem exists everywhere when our objective is to identify exceptional cases. The problem is handled by researchers either by modifying the existing classifications methods or by developing new methods. This paper review ensemble based approaches (Boosting and Bagging based) designed to address imbalance in classes by focusing on binary classification. We compared 6 Boosting based, 7 Bagging based and 2 hybrid ensembles for their performance in imbalance domain. We use KEEL tool to evaluate the performance of these methods by implementing the methods on seven imbalance data having class imbalance ratio from 1.82 to as high as 129.44. Area Under the curve (AUC) parameter is recorded as the performance metric. We also statistically analyzed the methods using Friedman rank test and Wilcoxon Matched Pair signed rank test to strengthen the visual interpretations. After analysis, it is proved that RusBoost ensemble outperformed every other ensemble in the imbalanced data situations.

Keywords: Ensemble approaches; boosting; bagging; hybrid ensembles; imbalanced data-sets; classification

Prabhjot Kaur and Anjana Gosain, “Empirical Assessment of Ensemble based Approaches to Classify Imbalanced Data in Binary Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 10(3), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100307

@article{Kaur2019,
title = {Empirical Assessment of Ensemble based Approaches to Classify Imbalanced Data in Binary Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100307},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100307},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {3},
author = {Prabhjot Kaur and Anjana Gosain}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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