Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
9-2023
Abstract
Unsupervised learning systems using clustering have gained significant attention for numerous applications due to their unique ability to discover patterns and structures in large unlabeled datasets. However, their effectiveness highly depends on their configuration, which requires domain-specific expertise and often involves numerous manual trials. Specifically, selecting appropriate algorithms and hyperparameters adds to the com- plexity of the configuration process. In this paper, we propose, apply, and assess an automated approach (AutoConf) for config- uring unsupervised learning systems using clustering, leveraging metamorphic testing and Bayesian optimization. Metamorphic testing is utilized to verify the configurations of unsupervised learning systems by applying a series of input transformations. We use Bayesian optimization guided by metamorphic-testing output to automatically identify the optimal configuration. The approach aims to streamline the configuration process and enhance the effectiveness of unsupervised learning systems. It has been evaluated through experiments on six datasets from three domains for anomaly detection. The evaluation results show that our approach can find configurations outperforming the baseline approaches as they achieved a recall of 0.89 and a precision of 0.84 (on average).
Keywords
AutoML, Unsupervised learning, Metamorphic Testing, Bayesian Optimization
Discipline
Information Security | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2023 38th IEEE/ACM International Conference on Automated Software Engineering: Luxembourg, September 11-15: Proceedings
First Page
1326
Last Page
1338
ISBN
9798350329964
Identifier
10.1109/ASE56229.2023.00094
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
SHAR, Lwin Khin; GOKNIL Arda; HUSOM, Erik Johannes; SEN, Sagar Sen; YAN, Naing Tun; and KIM, Kisub.
AutoConf: Automated configuration of unsupervised learning systems using metamorphic testing and Bayesian optimization. (2023). 2023 38th IEEE/ACM International Conference on Automated Software Engineering: Luxembourg, September 11-15: Proceedings. 1326-1338.
Available at: https://ink.library.smu.edu.sg/sis_research/8405
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/ASE56229.2023.00094