Reliability modeling for three-version machine learning systems through Bayesian networks

Qiang Wen and Fumio Machida

Reliability Engineering and System Safety, 2025, vol. 260, issue C

Abstract: Machine learning (ML) is extensively employed in AI-powered systems including safety-critical applications such as autonomous vehicles. The outputs from ML models are sensitive to real-world input data and error-prone, thereby improving the reliability of ML systems’ outputs has become a critical challenge in ML system design. In this paper, we introduce N-version ML architectures to enhance the ML system reliability and propose Bayesian Networks (BNs) models to evaluate the reliability of system outputs targeting three-version ML systems. The proposed BN reliability models allow us to formulate five distinct types of three-version ML architectures that are composed of diverse models and diverse input data sources. To validate the BN reliability models with real samples from ML systems, we conduct empirical studies on traffic sign recognition tasks and evaluate prediction performance. As a result, we find the prediction residuals between the observed reliability and the predicted reliability by the BN reliability models are less than 0.015 across all data sets, which is much better than the prediction performance by the baseline model. In addition, in comparison to the previous reliability models without exploiting BNs, the proposed models exhibit an advantage in reliability prediction, except for the triple model with single input architecture.

Keywords: Bayesian networks; Diversity; Machine learning system; N-version programming; Reliability (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0951832025002170
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:260:y:2025:i:c:s0951832025002170

DOI: 10.1016/j.ress.2025.111016

Access Statistics for this article

Reliability Engineering and System Safety is currently edited by Carlos Guedes Soares

More articles in Reliability Engineering and System Safety from Elsevier
Bibliographic data for series maintained by Catherine Liu ().