  Imbalanced datasets are ubiquitous. Classification performance on imbalanced
datasets is generally poor for the minority class as the classifier cannot
learn decision boundaries well. However, in sensitive applications like fraud
detection, medical diagnosis, and spam identification, it is extremely
important to classify the minority instances correctly. In this paper, we
present a novel technique based on genetic algorithms, GenSample, for
oversampling the minority class in imbalanced datasets. GenSample decides the
rate of oversampling a minority example by taking into account the difficulty
in learning that example, along with the performance improvement achieved by
oversampling it. This technique terminates the oversampling process when the
performance of the classifier begins to deteriorate. Consequently, it produces
synthetic data only as long as a performance boost is obtained. The algorithm
was tested on 9 real-world imbalanced datasets of varying sizes and imbalance
ratios. It achieved the highest F-Score on 8 out of 9 datasets, confirming its
ability to better handle imbalanced data compared to other existing
methodologies.
