Abstract
The approach of under-sampling the majority class is an effective method in dealing with classifying imbalanced data sets, but it has the deficiency of ignoring useful information. In order to eliminate this deficiency, we propose a Clustering Based Bagging Algorithm (CBBA). In CBBA, the majority class is clustered into several groups and instances are randomly sampled from each group. Those sampled instances are combined together with the minority class instances, and are used to train a base classifier. Final predictions are produced by combining those classifiers. The experimental results show that our approach outperforms the under-sampling method.
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Sun, XY., Zhang, HX., Wang, ZC. (2011). Clustering Based Bagging Algorithm on Imbalanced Data Sets. In: Tang, Y., Huynh, VN., Lawry, J. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2011. Lecture Notes in Computer Science(), vol 7027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24918-1_20
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DOI: https://doi.org/10.1007/978-3-642-24918-1_20
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