How distance metrics influence missing data imputation with k-nearest neighbours

摘要

In missing data contexts, k-nearest neighbours imputation has proven beneficial since it takes advantage of the similarity between patterns to replace missing values. When dealing with heterogeneous data, researchers traditionally apply the HEOM distance, that handles continuous, nominal and missing data. Although other heterogeneous distances have been proposed, they have not yet been investigated and compared for k-nearest neighbours imputation. In this work, we study the effect of several heterogeneous distances on k-nearest neighbours imputation on a large benchmark of publicly-available datasets.