Using Discretization for Extending the Set of Predictive Features
This work addresses a methodological issue in machine learning for practitioners by demonstrating that extending datasets with discretized features can enhance predictive accuracy, though it is incremental as it builds on existing discretization techniques.
The paper tackles the problem of improving predictive performance by proposing that discretized features should be added to, rather than replace, original continuous features, and presents D-MIAT, a supervised discretization algorithm that generates new features only when strong indications exist for target values. The results show that combining original data with features from D-MIAT and other algorithms yields the best performance on 28 benchmark datasets.
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.