Synthesizing Explainability Across Multiple ML Models for Structured Data

Explainable Machine Learning (XML) in high-stakes domains demands reproducible methods to aggregate feature importance across multiple models applied to the same structured dataset. We propose the Weighted Importance Score and Frequency Count (WISFC) framework, which combines importance magnitude an...

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Autors principals:	Emir Veledar, Lili Zhou, Omar Veledar, Hannah Gardener, Carolina M. Gutierrez, Jose G. Romano, Tatjana Rundek
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2025-06-01
Col·lecció:	Algorithms
Matèries:	explainable machine learning feature-importance aggregation ensemble interpretability small-data settings WISFC
Accés en línia:	https://www.mdpi.com/1999-4893/18/6/368
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