Interpretable Two-level Boolean Rule Learning for Classification
This work addresses the need for interpretable machine learning models, offering a domain-specific solution for classification tasks.
The paper tackles the problem of learning interpretable two-level Boolean rules for classification by proposing a novel optimization framework that balances classification accuracy and sparsity. The result is efficient algorithms that provide good tradeoffs, as demonstrated through experiments.
As a contribution to interpretable machine learning research, we develop a novel optimization framework for learning accurate and sparse two-level Boolean rules. We consider rules in both conjunctive normal form (AND-of-ORs) and disjunctive normal form (OR-of-ANDs). A principled objective function is proposed to trade classification accuracy and interpretability, where we use Hamming loss to characterize accuracy and sparsity to characterize interpretability. We propose efficient procedures to optimize these objectives based on linear programming (LP) relaxation, block coordinate descent, and alternating minimization. Experiments show that our new algorithms provide very good tradeoffs between accuracy and interpretability.