A survey of methodologies for the treatment of missing values within datasets: limitations and benefits

摘要

Knowledge discovery in ergonomics is complicated by the presence of missingndata, because most methodologies do not tolerate incomplete sample instances.nData-miners cannot always remove sample instances when they occur.nImputation methods are needed to ‘fill in’ estimated values for the missingninstances in order to construct a complete dataset. Even with emergingnmethodologies, the ergonomics field seems to rely on outdated imputationntechniques. This survey presents an overview of a variety of imputation methodsnfound in current academic research, which is not limited to ergonomic studies.nThe objective is to strengthen the communities’ understanding of imputationnmethodologies and briefly highlight their benefits and limitations. This surveynsuggests that the multiple imputation method is the current state-of-the-artnmissing value technique. This method has proven to be robust to many of thenshortcomings that plague other methods and should be considered the primarynchoice for missing value problems found in ergonomic studies.