A generative adversarial active learning method for effective outlier detection

- Bah, Mohamed, Zhang, Ji, Yu, Ting, Xia, Feng, Li, Zhao, Zhou, Shuigeng, Wang, Hongzhi

Title
A generative adversarial active learning method for effective outlier detection
Creator
Bah, Mohamed; Zhang, Ji; Yu, Ting; Xia, Feng; Li, Zhao; Zhou, Shuigeng; Wang, Hongzhi
Date
2022
Type
Text; Conference paper
Identifier
http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/192677
Identifier
vital:18026
Identifier
https://doi.org/10.1109/ICTAI56018.2022.00027
Identifier
ISBN:1082-3409 (ISSN); 9798350397444 (ISBN)
Abstract
Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem. © 2022 IEEE.
Publisher
IEEE Computer Society
Relation
34th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2022, Virtual, online, 31 October-2 November 2022, Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI Vol. 2022-October, p. 131-139
Rights
All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
Rights
Copyright ©2022 IEEE
Subject
Active learning; class-imbalance; Generative Adversarial Network; Outlier detection; Sampling
Reviewed
Funder
National Natural Science Foundation of China, NSFC
  • Hits: 771
  • Visitors: 686
  • Downloads: 0
Thumbnail File Description Size Format