Deep generative models to counter class imbalance: a model-metric mapping with proportion calibration methodology
Faculty / School
School of Mathematics and Computer Science (SMCS)
Department
Department of Computer Science
Was this content written or created while at IBA?
Yes
Document Type
Article
Source Publication
IEEE Access
ISSN
2169-3536
Keywords
Adversarial networks, Anomaly detection, Class imbalance, Deep generative models, Density estimation, Generative variational auto encoders, Instance hardness threshold, Machine learning best practices, Restricted Boltzmann machines
Disciplines
Computer Sciences | Engineering
Abstract
The most pervasive segment of techniques in managing class imbalance in machine learning are re-sampling-based methods. The emergence of deep generative models for augmenting the size of the under-represented class, prompts one to review the question of the suitability of the model chosen for data augmentation with the metric selected for the-goodness-of classification. This work defines this suitability by using newly-sampled data points from each generative model first to the degree of parity, and studying classification performance on a large set of metrics. We extend the investigation to different proportions of augmented data points for identifying the sensitivity of the metric to the degree of imbalance, leading to the discovery of an optimum proportion against the metric. The models used are GAN, VAE and RBM and the metrics include Precision, Recall, F1-Score, AUC, G-Mean and Balanced Accuracy. We offer a comparison of these models with the established class of data synthesizing counterparts on the aforementioned metrics. Deep generative models outperform the state-of-the-art on 5 metrics on multiple datasets and also comprehensively surpass the baselines. This work thereby recommends the following model-metric mappings: VAE for high Precision and F1-Score, RBM for high Recall and GAN for high AUC, G-Mean and Balanced Accuracy under various recommendedproportions of the minority class.
Indexing Information
HJRS - W Category, Scopus, Web of Science - Science Citation Index Expanded (SCI)
Journal Quality Ranking
Impact Factor: 3.367
Recommended Citation
Mirza, B., Haroon, D., Khan, B., Padhani, A., & Syed, T. Q. (2021). Deep generative models to counter class imbalance: a model-metric mapping with proportion calibration methodology. IEEE Access, 9, 55879-55897. Retrieved from https://ir.iba.edu.pk/faculty-research-articles/90
Publication Status
Published
COinS
