SMOTified-GAN for class imbalanced pattern classification problems. (arXiv:2108.03235v1 [cs.LG])

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 that has the synergy of SMOTE and GAN. The initial data
of minority class(es) generated by SMOTE is further enhanced by GAN that
produces better quality samples. 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.



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