Many natural processes generate some observations more frequently than others. These processes result in an imbalanced distributions which cause classifiers to bias toward the majority class because most classifiers assume a normal distribution. In order to address the problem of class imbalance, a number of data preprocessing techniques, which can be generally categorized into over-sampling and under-sampling methods, have been proposed throughout the years. The Neighborhood cleaning rule (NCL) method proposed by Laurikkala is among the most popular under-sampling methods. In this paper, we augment the original NCL algorithm by cleaning the unwanted samples using CHC evolutionary algorithm instead of a simple nearest neighborbased cleaning as in NCL. We name our augmented algorithm as NCL+. The performance of NCL+ is compared to that of NCL on 9 imbalanced datasets using 11 different classifiers. Experimental results show noticeable accuracy improvements by NCL+ over NCL. Moreover, NCL+ is also compared to another popular over-sampling method called Synthetic minority over-sampling technique (SMOTE), and is found to offer better results as well.
CITATION STYLE
Al Abdouli, N. O., Aung, Z., Woon, W. L., & Svetinovic, D. (2015). Tackling class imbalance problem in binary classification using augmented Neighborhood cleaning algorithm. Lecture Notes in Electrical Engineering, 339, 827–834. https://doi.org/10.1007/978-3-662-46578-3_98
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