Gaussian mixture based semi supervised boosting for imbalanced data classification

Semi supervised approaches are practical in problem domain where pattern clustering is supposed to provide good classification. Gaussian Mixture Model (GMM) can approximate arbitrary probability distribution, thus is considered as a dominant tool for classification in such domains. This paper appraises the functioning for GMM as it is applied to imbalanced datasets which consists of uneven distribution of samples from all the classes. Later, an ensemble approach is presented to boost the GMMs in a semi supervised manner via Adaptive Boosting technique. Experiment on benchmark imbalanced datasets with different imbalance ratio has been carried out. Empirical result demonstrates the efficacy of the proposed Boosted GMM classifier compared to baseline approaches like K-means and GMM.