To date, attribute discretization is typically performed by replacing the original set of continuous features with a transposed set of discrete ones. This paper provides support for a new idea that discretized features should often be used in addition to existing features and as such, datasets should be extended, and not replaced, by discretization. We also claim that discretization algorithms should be developed with the explicit purpose of enriching a non-discretized dataset with discretized values. We present such an algorithm, D-MIAT, a supervised algorithm that discretizes data based on minority interesting attribute thresholds. D-MIAT only generates new features when strong indications exist for one of the target values needing to be learned and thus is intended to be used in addition to the original data. We present extensive empirical results demonstrating the success of using D-MIAT on 28 benchmark datasets. We also demonstrate that 10 other discretization algorithms can also be used to generate features that yield improved performance when used in combination with the original non-discretized data. Our results show that the best predictive performance is attained using a combination of the original dataset with added features from a “standard” supervised discretization algorithm and D-MIAT.
|Journal||Eurasip Journal on Advances in Signal Processing|
|State||Published - 1 Dec 2018|
Bibliographical noteFunding Information:
The work by Avi Rosenfeld, Ron Illuz, and Dovid Gottesman was partially funded by the Charles Wolfson Charitable Trust.
© 2018, The Author(s).