RAMP: Rules Abstraction for Modeling and Prediction

Generating accurate and robust models is crucial to the successful use and deployment of classifiers on a large scale. Rule induction, i.e., generating decision rule models from data, is often a preferred approach to classification modeling and prediction, due to the enhanced explanatory capability and interpretability of decision rules. The RAMP system for rules abstraction and modeling is evolving with accuracy and robustness as primary goals. The system provides the following key capabilities: 1) feature analysis and selection based upon contextual merits technique, 2) “optimal” discretization of numerical features, 3) generation of minimal DNF (Disjunctive Normal Form) rules based upon the R-MINI algorithm, 4) rule based regression 5) rule pruning, weighting, and editing, 6) alternate rule application strategies, and 7) accuracy evaluation of the model on test data. In addition, RAMP also provides a hierarchical capability for case management, which helps end-users carry out multiple experiments on a data set, and manage these experiments as a set of related cases. RAMP has been utilized in several large-scale real-life applications and some benchmark tasks which demonstrate its robustness. We describe RAMP and its principal components in this paper.