Neural Symbolic Logical Rule Learner for Interpretable Learning
This addresses the need for more flexible and interpretable machine learning models, particularly in domains requiring transparent decision-making, though it appears incremental as it builds on existing rule-based neural network approaches.
The paper tackled the problem of inflexible rule-based neural networks for interpretable classification by introducing the Normal Form Rule Learner (NFRL) algorithm, which learns logical rules in CNF and DNF forms and demonstrated superior performance, rule quality, efficiency, and interpretability compared to 12 state-of-the-art alternatives on 11 datasets.
Rule-based neural networks stand out for enabling interpretable classification by learning logical rules for both prediction and interpretation. However, existing models often lack flexibility due to the fixed model structure. Addressing this, we introduce the Normal Form Rule Learner (NFRL) algorithm, leveraging a selective discrete neural network, that treat weight parameters as hard selectors, to learn rules in both Conjunctive Normal Form (CNF) and Disjunctive Normal Form (DNF) for enhanced accuracy and interpretability. Instead of adopting a deep, complex structure, the NFRL incorporates two specialized Normal Form Layers (NFLs) with adaptable AND/OR neurons, a Negation Layer for input negations, and a Normal Form Constraint (NFC) to streamline neuron connections. We also show the novel network architecture can be optimized using adaptive gradient update together with Straight-Through Estimator to overcome the gradient vanishing challenge. Through extensive experiments on 11 datasets, NFRL demonstrates superior classification performance, quality of learned rules, efficiency and interpretability compared to 12 state-of-the-art alternatives. Code and data are available at \url{https://anonymous.4open.science/r/NFRL-27B4/}.