Considering the Impact of Noise on Machine Learning Accuracy

Modern day smart cyber-physical systems (CPS) and Internet of Things (IoTs), including those deployed in critical devices such as wearables, often use embedded machine learning (ML). Owing to the consistent improvement in the overall performance of artificial neural networks (ANNs), the reliance of these systems on ANNs as an integral component has seen a constant rise. However, ANNs are known to be considerably vulnerable to noise. This, along with the noise being a ubiquitous component of the real-world environment, jeopardizes the accuracy of embedded ML-based systems. This calls for analyzing the impacts of noise on ANNs prior to their deployment in real-world ML-based system, to ensure acceptable ML accuracy. This chapter deals with the issue of analyzing the impacts of noise on trained ANNs. Multiple approaches for studying the impacts and possible noise models are discussed. Various impacts of noise, along with their formalization, on trained ANNs are elaborated. The chapter also provides a suitable framework for analyzing the impacts of noise. To demonstrate the impact of noise on an ANN trained on real-world data quantitatively, the framework is then used for the analysis of a binary classifier trained on genetic attributes of Leukemia patients.