Sharma, Anuraganand and Singh, Prabhat K. and Chandra, Rohitash (2022) SMOTified-GAN for class imbalanced pattern classification problems. IEEE Access, 10 . pp. 30655-30665. ISSN 2169-3536
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Abstract
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(N2d2T) for a sequential algorithm.
| Item Type: | Journal Article |
|---|---|
| Subjects: | Q Science > Q Science (General) > Q350-390 Information theory Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | School of Information Technology, Engineering, Mathematics and Physics (STEMP) |
| Depositing User: | Anuraganand Sharma |
| Date Deposited: | 30 Mar 2022 03:03 |
| Last Modified: | 30 Mar 2022 03:03 |
| URI: | https://repository.usp.ac.fj/id/eprint/13331 |
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