  An alternative to current mainstream preprocessing methods is proposed: Value
Selection (VS). Unlike the existing methods such as feature selection that
removes features and instance selection that eliminates instances, value
selection eliminates the values (with respect to each feature) in the dataset
with two purposes: reducing the model size and preserving its accuracy. Two
probabilistic methods based on information theory's metric are proposed: PVS
and P + VS. Extensive experiments on the benchmark datasets with various sizes
are elaborated. Those results are compared with the existing preprocessing
methods such as feature selection, feature transformation, and instance
selection methods. Experiment results show that value selection can achieve the
balance between accuracy and model size reduction.
