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An alternative to current mainstream preprocessing methods is proposed: ValueSelection (VS). Unlike the existing methods such as feature selection thatremoves features and instance selection that eliminates instances, valueselection eliminates the values (with respect to each feature) in the datasetwith two purposes: reducing the model size and preserving its accuracy. Twoprobabilistic methods based on information theory's metric are proposed: PVSand P + VS. Extensive experiments on the benchmark datasets with various sizesare elaborated. Those results are compared with the existing preprocessingmethods such as feature selection, feature transformation, and instanceselection methods. Experiment results show that value selection can achieve thebalance between accuracy and model size reduction.

Probabilistic Value Selection for Space Efficient Model