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To date, attribute discretization is typically performed by replacing theoriginal set of continuous features with a transposed set of discrete ones.This paper provides support for a new idea that discretized features shouldoften be used in addition to existing features and as such, datasets should beextended, and not replaced, by discretization. We also claim thatdiscretization algorithms should be developed with the explicit purpose ofenriching a non-discretized dataset with discretized values. We present such analgorithm, D-MIAT, a supervised algorithm that discretizes data based onMinority Interesting Attribute Thresholds. D-MIAT only generates new featureswhen strong indications exist for one of the target values needing to belearned and thus is intended to be used in addition to the original data. Wepresent extensive empirical results demonstrating the success of using D-MIATon $ 28 $ benchmark datasets. We also demonstrate that $ 10 $ otherdiscretization algorithms can also be used to generate features that yieldimproved performance when used in combination with the original non-discretizeddata. Our results show that the best predictive performance is attained using acombination of the original dataset with added features from a "standard"supervised discretization algorithm and D-MIAT.

Using Discretization for Extending the Set of Predictive Features