Topographic under-sampling for unbalanced distributions

Several aspects could affect the existing machine learning algorithms. One of these aspects is related to unbalanced classes in which the number of observations belonging to a class, greatly exceeds the observations in other classes. We propose in this paper an under-sampling method which uses self-organizing map to cluster the majority class guided with minority class. The proposed approach has been validated on multiple data sets using decision trees as a classifier with cross validation. The experimental results showed that elimination from majority class by integrating Neighborhood Cleaning Rule in SOM algorithm, produce high and very promising performance.