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
Tables : 05
Mr. L. Hakim : master student in Department of Statistics, Bogor
Agricultural University. His main interests is on data mining and
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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