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Modular Local Classification via Cluster-Guided Feature Selection in Tabular Data
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DOI：10.18267/j.aip.295.png)
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Abstract
En 中文
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.
Keywords:
Local models
Feature selection
Clustering
Modular classification
Tabular data interpretable machine learning
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