TY - JOUR
T1 - Neural Logic Networks for Interpretable Decision Rule Learning: Alternative Formulations and Architectures
AU - Ali Hassan
AU - Noor Siddiqui
AU - Bilal Khan
AU - Sana Malik
AU - Hassan Ali
JF - Journal of Trustworthy and Explainable Intelligent Systems
JO - J Trustworthy Explain Intell Syst
Y1 - 2025
VL - 4
IS - 2
SP - 69
N2 - This study develops an expanded framework for extracting classification rules from neural network architectures applied to tabular data. We introduce alternative designs for learning Boolean logic operators by configuring neurons with continuous weights, including the integration of trainable NAND gates. This unified formulation ensures that distinct logical operations are trained under a singular mathematical approach, allowing developers to directly deploy established sparsity-inducing optimization routines—such as reweighted L1 regularization—to simplify the resulting network into concise rule sets. Furthermore, by utilizing De Morgan’s law, we construct an alternative network topology based on these trainable NAND components to create a NAND-NAND structure rather than the traditional AND-OR setup, both of which easily convert into discrete decision rules. Empirical evaluations show that these revised methodologies produce highly competitive decision rule models that match state-of-the-art predictive accuracy on tabular datasets.
UR - https://tipublications.com/i628931859
ER -