Z-Score Rule Induction: An Interpretable Lightweight Classifier

Extracting interpretable rules to retrieve rare target instances from large unlabeled populations is a critical challenge when confirmed positive cases are extremely scarce. While decision trees offer interpretability, they overfit to the small set of positive examples and fail to generalize to unseen positive instances. To address this, we propose Z-Score Rule Induction (ZSRI), a lightweight algorithm specifically designed to induce simple, interpretable rules from tiny positive seeds in a single pass. ZSRI ranks features by their positive-class deviations using univariate $z$-scores and induces decision boundaries via extremum- or quantile-based strategies to encompass target positive instances. Experimental evaluations on Wisconsin Breast Cancer, Pima Indians Diabetes, and synthetic benchmarks demonstrate that ZSRI achieves competitive predictive performance while executing up to $28\times$ faster than decision trees. Furthermore, on the Wisconsin Breast Cancer dataset, ZSRI trained with as few as $10$ positive seed instances recovers up to $83.9\%$ of unseen positive cases while maintaining a high precision of $96.7\%$.