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Semi-Supervised Root-Cause Analysis with Co-Training for Integrated Systems

Publication ,  Conference
Pan, R; Li, X; Chakrabarty, K
Published in: Proceedings of the IEEE VLSI Test Symposium
January 1, 2022

The increasing complexity of integrated systems has exacerbated the challenges associated with system diagnosis. To tackle these challenges, intelligent root-cause-analysis facilitated by machine learning has been proposed in recent years. However, most of these methods rely on a large amount of data with root-cause labels, which are often either not available or difficult to obtain. In this paper, we propose a semi-supervised root-cause-analysis method with co-training, where only a small set of labeled data is required. Using random forest as the learning kernel, a co-training technique is proposed to leverage the unlabeled data by automatically pre-labeling a subset of them and retraining each decision tree. In addition, several novel techniques are proposed to avoid over-fitting and determine hyper-parameters. Two case studies based on industrial designs demonstrate that the proposed approach significantly outperforms state-of-the-art methods by saving up to 43% of labeling efforts by human experts.

Duke Scholars

Published In

Proceedings of the IEEE VLSI Test Symposium

DOI

ISBN

9781665410601

Publication Date

January 1, 2022

Volume

2022-April
 

Citation

APA
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MLA
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Pan, R., Li, X., & Chakrabarty, K. (2022). Semi-Supervised Root-Cause Analysis with Co-Training for Integrated Systems. In Proceedings of the IEEE VLSI Test Symposium (Vol. 2022-April). https://doi.org/10.1109/VTS52500.2021.9794192
Pan, R., X. Li, and K. Chakrabarty. “Semi-Supervised Root-Cause Analysis with Co-Training for Integrated Systems.” In Proceedings of the IEEE VLSI Test Symposium, Vol. 2022-April, 2022. https://doi.org/10.1109/VTS52500.2021.9794192.
Pan R, Li X, Chakrabarty K. Semi-Supervised Root-Cause Analysis with Co-Training for Integrated Systems. In: Proceedings of the IEEE VLSI Test Symposium. 2022.
Pan, R., et al. “Semi-Supervised Root-Cause Analysis with Co-Training for Integrated Systems.” Proceedings of the IEEE VLSI Test Symposium, vol. 2022-April, 2022. Scopus, doi:10.1109/VTS52500.2021.9794192.
Pan R, Li X, Chakrabarty K. Semi-Supervised Root-Cause Analysis with Co-Training for Integrated Systems. Proceedings of the IEEE VLSI Test Symposium. 2022.

Published In

Proceedings of the IEEE VLSI Test Symposium

DOI

ISBN

9781665410601

Publication Date

January 1, 2022

Volume

2022-April