  Class imbalance in a dataset is a major problem for classifiers that results
in poor prediction with a high true positive rate (TPR) but a low true negative
rate (TNR) for a majority positive training dataset. Generally, the
pre-processing technique of oversampling of minority class(es) are used to
overcome this deficiency. Our focus is on using the hybridization of Generative
Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique
(SMOTE) to address class imbalanced problems. We propose a novel two-phase
oversampling approach involving knowledge transfer that has the synergy of
SMOTE and GAN. The unrealistic or overgeneralized samples of SMOTE are
transformed into realistic distribution of data by GAN where there is not
enough minority class data available for GAN to process them by itself
effectively. We named it SMOTified-GAN as GAN works on pre-sampled minority
data produced by SMOTE rather than randomly generating the samples itself. The
experimental results prove the sample quality of minority class(es) has been
improved in a variety of tested benchmark datasets. Its performance is improved
by up to 9\% from the next best algorithm tested on F1-score measurements. Its
time complexity is also reasonable which is around $O(N^2d^2T)$ for a
sequential algorithm.
