  Decision making from data involves identifying a set of attributes that
contribute to effective decision making through computational intelligence. The
presence of missing values greatly influences the selection of right set of
attributes and this renders degradation in classification accuracies of the
classifiers. As missing values are quite common in data collection phase during
field experiments or clinical trails appropriate handling would improve the
classifier performance. In this paper we present a review of recently developed
missing value imputation algorithms and compare their performance on some bench
mark datasets.
