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Ensemble methods for supervised machine learning have become popular due totheir ability to accurately predict class labels with groups of simple,lightweight "base learners." While ensembles offer computationally efficientmodels that have good predictive capability they tend to be large and offerlittle insight into the patterns or structure in a dataset. We consider anensemble technique that returns a model of ranked rules. The model accuratelypredicts class labels and has the advantage of indicating which parameterconstraints are most useful for predicting those labels. An example of the ruleensemble method successfully ranking rules and selecting attributes is givenwith a dataset containing images of potential supernovas where the number ofnecessary features is reduced from 39 to 21. We also compare the rule ensemblemethod on a set of multi-class problems with boosting and bagging, which aretwo well known ensemble techniques that use decision trees as base learners,but do not have a rule ranking scheme.

Dimension Reduction Using Rule Ensemble Machine Learning Methods: A Numerical Study of Three Ensemble Methods