Entropy-driven topology mapping framework for robust Bayesian classification

Yang Liu

doi:10.1016/j.knosys.2026.115378

2026-01-24

PRE

Yang Liu *

Qi Wu

Xu Zhang

DOI：10.1016/j.knosys.2026.115378

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摘要

En 中文

Balancing predictive accuracy with model interpretability remains a fundamental challenge in artificial intelligence and machine learning. Bayesian network classifiers (BNCs) address this by leveraging directed acyclic graphs (DAGs) to explicitly represent probabilistic dependencies. Current BNCs fall into two categories: single-topology methods, which struggle with inter-class divergence and intra-class heterogeneity in modern datasets, and multi-topology ensemble methods, which often employ oversimplified ensemble strategies and rely on pairwise dependency metrics that neglect synergistic effects and global coherence. To overcome these limitations, this paper proposes the entropy-driven topology mapping Bayesian classifier (ETMBC). It introduces a novel two-stage learning strategy guided by dual entropy minimization: class-specific entropy captures general dependencies across class distributions to handle inter-class divergence, while instance-specific entropy models specialized dependencies within testing instances. For classification, we develop a novel topology mapping approach that dynamically integrates predictions from multiple DAGs by jointly optimizing Jensen-Shannon divergence for structural consistency evaluation and topological information gain for complementary dependency discovery, effectively addressing intra-class heterogeneity and dependency asymmetry without predefined rules. Extensive experimental evaluation on 50 publicly available datasets spanning various domains with distinct properties demonstrate that ETMBC consistently outperforms state-of-the-art single-topology and multi-topology learners in terms of zero-one loss, bias, variance, root-mean-square error, and statistical tests.