  Imbalance in the proportion of training samples belonging to different
classes often poses performance degradation of conventional classifiers. This
is primarily due to the tendency of the classifier to be biased towards the
majority classes in the imbalanced dataset. In this paper, we propose a novel
three step technique to address imbalanced data. As a first step we
significantly oversample the minority class distribution by employing the
traditional Synthetic Minority OverSampling Technique (SMOTE) algorithm using
the neighborhood of the minority class samples and in the next step we
partition the generated samples using a Gaussian-Mixture Model based clustering
algorithm. In the final step synthetic data samples are chosen based on the
weight associated with the cluster, the weight itself being determined by the
distribution of the majority class samples. Extensive experiments on several
standard datasets from diverse domains shows the usefulness of the proposed
technique in comparison with the original SMOTE and its state-of-the-art
variants algorithms.
