'Institute of Electrical and Electronics Engineers (IEEE)'
Doi
Abstract
The performance evaluation of imputation algorithms often involves the generation of missing
values. Missing values can be inserted in only one feature (univariate con guration) or in several features
(multivariate con guration) at different percentages (missing rates) and according to distinct missing
mechanisms, namely, missing completely at random, missing at random, and missing not at random. Since
the missing data generation process de nes the basis for the imputation experiments (con guration, missing
rate, and missing mechanism), it is essential that it is appropriately applied; otherwise, conclusions derived
from ill-de ned setups may be invalid. The goal of this paper is to review the different approaches to
synthetic missing data generation found in the literature and discuss their practical details, elaborating on
their strengths and weaknesses. Our analysis revealed that creating missing at random and missing not at
random scenarios in datasets comprising qualitative features is the most challenging issue in the related
work and, therefore, should be the focus of future work in the field