A1048
Title: Imputations in one-shot devices data using machine learning algorithms
Authors: Hon Yiu So - Oakland University (United States) [presenting]
Abstract: One-shot devices are products that will be destroyed immediately after use. Most of them have multiple components. Malfunctioning any one of the components will result in the device's failure. The one-shot devices are often tested under constant stress, accelerated life-test, or collect data from users or surveys to assess such devices. A link function relating to stress levels and lifetime is then applied to extrapolate the lifetimes of units from accelerated conditions to normal operating conditions. However, missing data often occurs during the data collection, and imputation is a popular way to analyze this data. The aim is to explore imputation performance using machine learning algorithms on one-shot datasets and compare them to traditional imputation methods.
