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This research investigates and refines the MINDWALC node classification algorithm, specifically targeting its capacity to extract human-interpretable decision trees from knowledge graph (KG) architectures. We propose techniques to tailor MINDWALC for environments where the knowledge graph is bifurcated into internal background knowledge (domain-specific data) and instance knowledge (specific case data). While the core logic of MINDWALC—utilizing discriminative graph walks as features—is preserved, we introduce two primary enhancements. First, we implement relation-tail merging to empower the algorithm to process relation-modified nodes. Second, we propose flexible walking depths that, when combined with the original MINDWALC strategy, improve the detection of commonalities between node instances. Our findings suggest that these flexible walks can markedly increase classification accuracy in hierarchical or incomplete tree-like graphs, though performance varies on more heterogeneous KG structures. Ultimately, this work demonstrates that our refinements significantly boost MINDWALC’s utility in tree-structured environments, allowing the system to use background knowledge to bridge gaps in instance data in a transparent way. Initial evaluations on medical toy datasets suggest these optimizations could refine clinical decision-making, particularly in sectors requiring explainable artificial intelligence.

Deep learning (DL) architectures have established state-of-the-art capabilities across numerous fields. Unpacking their internal operations and reasoning paths is critical due to their high architectural complexity and opaque, black-box character, particularly in high-stakes fields like healthcare. Visual analytics (VA) integrated with DL methodologies are frequently employed to extract data insights, yet they regularly face challenges related to visual clutter (VC). This research introduces a streamlined neural network (NN) view design engineered to mitigate visual clutter while clarifying DL model elements for field specialists and everyday users. We applied clustering techniques to aggregate hidden neurons according to similarities in their activation profiles. This framework accommodates both macroscopic and micro-level perspectives of the generated neuron groups. Utilizing a tabular medical dataset as a practical case study, this clustered visualization strategy successfully decreased visual clutter by 54% for neuron graphics and by 88.7% for their interconnections, while successfully exposing correlated neuron activations developed throughout model training.

In recent years, considerable attention has been directed toward clarifying complex, opaque systems, commonly referred to as black-box models, including deep neural networks and random forests. So-called model-agnostic techniques generally approximate the outputs of these non-transparent systems using an interpretable architecture, ignoring the internal mechanisms of the primary black-box system itself. Consequently, it remains an important inquiry whether the straightforward training of transparent, white-box architectures ought to be favored over the post-hoc approximation of these opaque models. This study presents the findings of an empirical investigation comparing post-hoc explanatory methods against inherently interpretable architectures across multiple datasets, focusing on both rule-based and feature-based frameworks. The empirical outcomes indicate that directly trained transparent models frequently approximate black-box models with an accuracy that matches or exceeds that of post-hoc surrogate models, despite lacking direct access to the underlying black-box structure.

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.