Acta Informatica Pragensia | 2026
Authors: Boussaad L.
DOI: 10.18267/J.AIP.295
Journal: Acta Informatica Pragensia
Year: 2026
Publisher: Prague University of Economics and Business
Document Type: Article
Open Access: All Open Access; Gold Open Access; Green Open Access
Cited by: 0
Background: Many real-world tabular datasets are heterogeneous, with distinct regions of the feature space exhibiting different feature–label relationships. Conventional global classifiers often miss these local patterns, reducing both predictive accuracy and interpretability. Objective: This study aims to design a modular classification framework that combines local specialization with global consistency to enhance predictive performance and interpretability in heterogeneous tabular data. Methods: The author proposes Cluster-guided local feature selection with top-2 voting and fallback (CGLFS+), which integrates unsupervised clustering, cluster-specific feature selection and lightweight local models. Final predictions combine top-2 local decisions with a global fallback classifier for robustness. The framework was evaluated on five diverse benchmark datasets using repeated stratified cross-validation. Results: CGLFS+ achieved consistent gains in accuracy and macro F1 over strong baselines, with statistically significant improvements and competitive inference times. Conclusion: CGLFS+ successfully balances local adaptation and global consistency, providing a scalable and interpretable approach well suited to heterogeneous domains such as healthcare, chemistry and finance. Copyright: © 2026 by the author(s). Licensee Prague University of Economics and Business, Czech Republic. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (CC BY 4.0).
Clustering; Feature selection; Index Terms Local models; Modular classification; Tabular data interpretable machine learning