The Usage of the Relief Algorithm for Edit and Imputation in the Canadian Census of Population
Articles and reports: 11-522-X202500100035Description: Historically, the Canadian census of population Edit and Imputation (E&I) process has operated using a nearest-neighbour donor imputation methodology wherein the distance between a failed unit and a potential donor is obtained through a weighted combination of auxiliary variables. Revision to the model between cycles can be a complicated and time-consuming process given there is no standard approach to variable selection and weighting between topics. This paper will illustrate the potential of the Relief variable selection algorithm to create a machine learning-driven approach to variable selection and weighting that is standardized and comparable between census cycles and among the many topics of the census. An overview on how this process may be applied in practice will be presented, followed by results on several topics that indicate a general improvement over previous methods.
Issue Number: 2025001Author(s): Khuu, IrwinMain Product:Statistics Canada International Symposium Series: Proceedings
