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Modular Local Classification via Cluster-Guided Feature Selection in Tabular Data

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...

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Hovedforfatter: Leila Boussaad
Format: Artigo
Sprog:Inglês
Udgivet: Prague University of Economics and Business 2026-01-01
Serier:Acta Informatica Pragensia
Fag:
local models
feature selection
clustering
modular classification
tabular data interpretable machine learning
Online adgang:https://aip.vse.cz/artkey/aip-202601-0010_modular-local-classification-via-cluster-guided-feature-selection-in-tabular-data.php
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