  Probabilistic mixture models have been widely used for different machine
learning and pattern recognition tasks such as clustering, dimensionality
reduction, and classification. In this paper, we focus on trying to solve the
most common challenges related to supervised learning algorithms by using
mixture probability distribution functions. With this modeling strategy, we
identify sub-labels and generate synthetic data in order to reach better
classification accuracy. It means we focus on increasing the training data
synthetically to increase the classification accuracy.
