{"title":"Root-Cause Analysis with Semi-Supervised Co-Training for Integrated Systems","authors":"Renjian Pan, Xin Li, Krishnendu Chakrabarty","doi":"10.1145/3649313","DOIUrl":null,"url":null,"abstract":"<p>Root-cause analysis for integrated systems has become increasingly challenging due to their growing complexity. To tackle these challenges, machine learning (ML) has been applied to enhance root-cause analysis. Nonetheless, ML-based root-cause analysis usually requires abundant training data with root causes labeled by human experts, which are difficult or even impossible to obtain. To overcome this drawback, a semi-supervised co-training method is proposed for root-cause-analysis in this paper, which only requires a small portion of labeled data. First, a random forest is trained with labeled data. Next, we propose a co-training technique to learn from unlabeled data with semi-supervised learning, which pre-labels a subset of these data automatically and then retrains each decision tree in the random forest. In addition, a robust framework is proposed to avoid over-fitting. We further apply initialization by clustering and feature selection to improve the diagnostic performance. With two case studies from industry, the proposed approach shows superior performance against other state-of-the-art methods by saving up to 67% of labeling efforts.</p>","PeriodicalId":50944,"journal":{"name":"ACM Transactions on Design Automation of Electronic Systems","volume":null,"pages":null},"PeriodicalIF":2.2000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Design Automation of Electronic Systems","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1145/3649313","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Root-cause analysis for integrated systems has become increasingly challenging due to their growing complexity. To tackle these challenges, machine learning (ML) has been applied to enhance root-cause analysis. Nonetheless, ML-based root-cause analysis usually requires abundant training data with root causes labeled by human experts, which are difficult or even impossible to obtain. To overcome this drawback, a semi-supervised co-training method is proposed for root-cause-analysis in this paper, which only requires a small portion of labeled data. First, a random forest is trained with labeled data. Next, we propose a co-training technique to learn from unlabeled data with semi-supervised learning, which pre-labels a subset of these data automatically and then retrains each decision tree in the random forest. In addition, a robust framework is proposed to avoid over-fitting. We further apply initialization by clustering and feature selection to improve the diagnostic performance. With two case studies from industry, the proposed approach shows superior performance against other state-of-the-art methods by saving up to 67% of labeling efforts.
由于集成系统日益复杂,对其进行根本原因分析变得越来越具有挑战性。为了应对这些挑战,人们应用机器学习(ML)来加强根源分析。然而,基于 ML 的根本原因分析通常需要大量由人类专家标注根本原因的训练数据,而这些数据很难甚至不可能获得。为了克服这一缺点,本文提出了一种用于根源分析的半监督协同训练方法,它只需要一小部分标注数据。首先,使用标注数据训练随机森林。接下来,我们提出了一种通过半监督学习从无标签数据中学习的联合训练技术,该技术会自动预标记这些数据的一个子集,然后重新训练随机森林中的每一棵决策树。此外,我们还提出了一个稳健的框架,以避免过度拟合。我们进一步通过聚类和特征选择进行初始化,以提高诊断性能。通过对两个行业案例的研究,我们提出的方法与其他最先进的方法相比表现出更优越的性能,最多可节省 67% 的标注工作。
期刊介绍:
TODAES is a premier ACM journal in design and automation of electronic systems. It publishes innovative work documenting significant research and development advances on the specification, design, analysis, simulation, testing, and evaluation of electronic systems, emphasizing a computer science/engineering orientation. Both theoretical analysis and practical solutions are welcome.