The BBG Rule Induction Algorithm

We present an algorithm (BBG) for inductive learning from examples that outputs a rule list. BBG uses a combination of greedy and branch-and-bound techniques, and naturally handles noisy or stochastic learning situations. We also present the results of an empirical study comparing BBG with Quinlan's C4.5 on 1050 synthetic data sets. We nd that BBG greatly outperforms C4.5 on rule-oriented problems, and equals or exceeds C4.5's performance on tree-oriented problems.