Missing values make pattern analysis difficult, particularly with limited available data. In longitudinal research,
missing values accumulate, thereby aggravating the problem. Here we consider how to deal with temporal
data with missing values in handwriting analysis. In the task of studying development of individuality of
handwriting, we encountered the fact that feature values are missing for several individuals at several time
instances. Six algorithms, i.e., random imputation, mean imputation, most likely independent value imputation,
and three methods based on Bayesian network (static Bayesian network, parameter EM, and structural EM),
are compared with children's handwriting data. We evaluate the accuracy and robustness of the algorithms
under different ratios of missing data and missing values, and useful conclusions are given. Specifically, static
Bayesian network is used for our data which contain around 5% missing data to provide adequate accuracy and
low computational cost.
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