Lightweight Rule Induction

A lightweight rule induction method is described that generates compact Disjunctive Normal Form (DNF) rules. Each class has an equal number of unweighted rules. A new example is classi ed by applying all rules and assigning the example to the class with the most satis ed rules. The induction method attempts to minimize the training error with no pruning. An overall design is speci ed by setting limits on the size and number of rules. During training, cases are adaptively weighted using a simple cumulative error method. The induction method is nearly linear in time relative to an increase in the number of induced rules or the number of cases. Experimental results on large benchmark data sets demonstrate that predictive performance can rival the best reported results in the literature.