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  Bagging Based Ensemble Classification Method on Imbalance Datasets  
  Authors : Lukmanul Hakim; Bagus Sartono; Asep Saefuddin
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In the last few years, the problem of class imbalances is a challenging problem in data mining community. The class imbalance occurs when one of the classes in the data has a larger number than others. That condition causing the classification being not optimum because the larger class gave more influences in the classification. Some cases of class imbalance issues become a very important thing, for example, to detect cheating in banking operations, network trouble, cancer diagnose, and prediction of technical failure. This study conducts a bagging based ensemble method to overcome the problem of class imbalance on 14 datasets. The purpose of this research is to see the ability of some bagging based ensemble methods on overcoming the class imbalance problem. The results obtained by using OverBagging method are more stable than other bagging based methods in various datasets.


Published In : IJCSN Journal Volume 6, Issue 6

Date of Publication : December 2017

Pages : 670-676

Figures :04

Tables : 05


Mr. L. Hakim : master student in Department of Statistics, Bogor Agricultural University. His main interests is on data mining and bioinformatics.

Dr. B. Sartono : Currently worked as a lecture in Department of Statistics, Bogor Agricultural University. His main interests is on data mining and experimental design.

A. Saefuddin : received the M.Sc. and Ph.D.. In University of Guelph, Canada. He is a professor in Department of Statistics, Bogor Agricultural University. He is also serving as the Rector of Al – Azhar University Indonesia in Jakarta. His expertize is on genetic and biostatistics.


Ensemble, Boosting, Bagging, Class Imbalance, Classification

Overall, bagging based methods can improve results in minority classes as evidenced by their higher sensitivity values compared to the CART method. Although the overall value of specificity in the CART method is superior to that of the bagging method. This illustrates that the CART method is not able to predict the minority class well. The OverBagging method is a stable method for various datasets in both extreme and non-extreme classes. However, OverBagging method takes a long time in computing process. Another stable method is the Roughly Balanced Bagging method because the Roughly Balanced Bagging method as a whole is able to predict the minority class better when compared to other methods except in the extreme data Bagging Ensemble Variation is better when compared with the method of Roughly Balanced Bagging. But the Bagging Ensemble Variation not incapable of predicting trees with equal number of opportunities.


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