An official website of the United States government
In this paper, we introduce DuelGAN, a generative adversarial network (GAN) solution to improve the stability of the generated samples and to mitigate mode collapse. Built upon the Vanilla GAN’s two-player game between the discriminator D1 and the generator G, we introduce a peer discriminator D2 to the min-max game. Similar to previous work using two discriminators, the first role of both D1, D2 is to distinguish between generated samples and real ones, while the generator tries to generate high-quality samples which are able to fool both discriminators. Different from existing methods, we introduce a duel between D1 and D2 to discourage their agreement and therefore increase the level of diversity of the generated samples. This property alleviates the issue of early mode collapse by preventing D1 and D2 from converging too fast. We provide theoretical analysis for the equilibrium of the min-max game formed among G,D1,D2. We offer convergence behavior of DuelGAN as well as stability of the min-max game. It’s worth mentioning that DuelGAN operates in the unsupervised setting, and the duel between D1 and D2 does not need any label supervision. Experiments results on a synthetic dataset and on real-world image datasets (MNIST, Fashion MNIST, CIFAR-10, STL-10, CelebA, VGG) demonstrate that DuelGAN outperforms competitive baseline work in generating diverse and high-quality samples, while only introduces negligible computation cost. Our code is publicly available at https://github.com/UCSC-REAL/DuelGAN.
more »
« less
Examining Effects of Class Imbalance on Conditional GAN Training
In this work, we investigate the impact of class imbalance on the accuracy and diversity of synthetic samples generated by conditional generative adversarial networks (CGAN) models. Though many studies utilizing GANs have seen extraordinary success in producing realistic image samples, these studies generally assume the use of well-processed and balanced benchmark image datasets, including MNIST and CIFAR-10. However, well-balanced data is uncommon in real world applications such as detecting fraud, diagnosing diabetes, and predicting solar flares. It is well known that when class labels are not distributed uniformly, the predictive ability of classification algorithms suffers significantly, a phenomenon known as the "class-imbalance problem." We show that the imbalance in the training set can also impact sample generation of CGAN models. We utilize the well known MNIST datasets, controlling the imbalance ratio of certain classes within the data through sampling. We are able to show that both the quality and diversity of generated samples suffer in the presence of class imbalances and propose a novel framework named Two-stage CGAN to produce high-quality synthetic samples in such cases. Our results indicate that the proposed framework provides a significant improvement over typical oversampling and undersampling techniques utilized for class imbalance remediation.
more »
« less
- Award ID(s):
- 1931555
- PAR ID:
- 10475339
- Editor(s):
- Rutkowski L.; Scherer R.; Korytkowski M.; Pedrycz W.; Tadeusiewicz R.; Zurada J.
- Publisher / Repository:
- Artificial Intelligence and Soft Computing, Series: Lecture Notes in Computer Science (LNCS), Vol. 14125, Springer-Verlag, 2023
- Date Published:
- Journal Name:
- Proceedings of the 22nd International Conference on Artificial Intelligence and Soft Computing, (ICAISC).
- ISBN:
- 978-3-031-42504-2
- Format(s):
- Medium: X
- Location:
- Zakopane, Poland
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
This research presents a hybrid intrusion detection approach that integrates Generative Adversarial Networks (GANs) for synthetic data generation with Random Forest (RF) as the primary classifier. The study aims to improve detection performance in cybersecurity applications by enhancing dataset diversity and addressing challenges in traditional models, particularly in detecting minority attack classes often underrepresented in real-world datasets. The proposed method employs GANs to generate synthetic attack samples that mimic real-world intrusions, which are then combined with real data from the UNSW-NB15 dataset to create a more balanced training set. By leveraging synthetic data augmentation, our approach mitigates issues related to class imbalance and enhances the generalization capability of the classifier. Extensive experiments demonstrate that RF trained on the combined dataset of real and synthetic data achieves superior detection performance compared to models trained exclusively on real data. Specifically, RF trained solely on the original dataset achieves an accuracy of 97.58%, whereas integrating GAN-generated synthetic data improves accuracy to 98.27%. The proposed methodology is further evaluated through comparative analysis against alternative classifiers, including Support Vector Machine (SVM), XGBoost, Gated Recurrent Unit (GRU), and related studies in the field. Our findings indicate that GAN-augmented training significantly enhances detection rates, particularly for rare attack types, while maintaining computational efficiency. Furthermore, RF outperforms other classifiers, including deep learning models, demonstrating its effectiveness as a lightweight yet robust classification method. Integrating GANs with RF offers a scalable and adaptable framework for intrusion detection, ensuring improved resilience against evolving cyber threats.more » « less
-
In the field of healthcare, electronic health records (EHR) serve as crucial training data for developing machine learning models for diagnosis, treatment, and the management of healthcare resources. However, medical datasets are often imbalanced in terms of sensitive attributes such as race/ethnicity, gender, and age. Machine learning models trained on class-imbalanced EHR datasets perform significantly worse in deployment for individuals of the minority classes compared to those from majority classes, which may lead to inequitable healthcare outcomes for minority groups. To address this challenge, we propose Minority Class Rebalancing through Augmentation by Generative modeling (MCRAGE), a novel approach to augment imbalanced datasets using samples generated by a deep generative model. The MCRAGE process involves training a Conditional Denoising Diffusion Probabilistic Model (CDDPM) capable of generating high-quality synthetic EHR samples from underrepresented classes. We use this synthetic data to augment the existing imbalanced dataset, resulting in a more balanced distribution across all classes, which can be used to train less biased downstream models. We measure the performance of MCRAGE versus alternative approaches using Accuracy, F1 score and AUROC of these downstream models. We provide theoretical justification for our method in terms of recent convergence results for DDPMs.more » « less
-
Rubin, Stuart; Chen, Shu-Ching (Ed.)In this work, we use an unsupervised method for generating binary class labels in a novel context to create class labels for Medicare fraud detection. We examine how class imbalance influences the quality of these new labels and how it affects supervised classification. We use four different Medicare Part D fraud detection datasets, with the largest containing over 5 million instances. The other three datasets are sampled from the original dataset. Using Random Under-Sampling (RUS), we subsample from the majority class of the original data to produce three datasets with varying levels of class imbalance. To evaluate the performance of the newly created labels, we train a supervised classifier and evaluate its classification performance and compare it to an unsupervised anomaly detection method as a baseline. Our empirical findings indicate that the generated class labels are of high enough quality and enable effective supervised classifier training for fraud detection. Additionally, supervised classification with the new labels consistently outperforms the baseline used for comparison across all test scenarios. Further more, we observe an inverse relationship between class imbalance in the dataset and classifier performance, with AUPRC scores improving as the training dataset becomes more balanced. This work not only validates the efficacy of the synthesized class labels in labeling Medicare fraud but also shows its robustness across different degrees of class imbalance.more » « less
-
The rapid growth of Internet of Things (IoT) devices has increased the need for effective network-based anomaly detection to identify malicious activities such as botnet communication and distributed attacks. While machine learning techniques have shown promising results in IoT intrusion detection, their effectiveness strongly depends on dataset quality and class distribution. This work investigates the impact of extreme class imbalance on IoT anomaly detection using the IoT-23 dataset. Network traffic features are extracted from packet capture data using fixed 30-second windows, and both statistical rulebased detection and Random Forest classification are evaluated. Experimental results show that rule-based methods tend to over-detect anomalies in highly variable malware traffic, while supervised machine learning models can achieve misleadingly high accuracy under severe class imbalance. Furthermore, class weighting and oversampling fail to adequately address the imbalance due to the limited diversity of minority-class samples. These findings demonstrate that dataset characteristics can significantly influence detection performance and highlight the need for more robust data collection, evaluation metrics, and learning strategies for reliable IoT forensic analysis.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1007/978-3-031-42505-9_40
