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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Bibliographic Details
Main Authors:	Emir Veledar, Lili Zhou, Omar Veledar, Hannah Gardener, Carolina M. Gutierrez, Jose G. Romano, Tatjana Rundek
Format:	Article
Language:	English
Published:	MDPI AG 2025-06-01
Series:	Algorithms
Subjects:	explainable machine learning feature-importance aggregation ensemble interpretability small-data settings WISFC
Online Access:	https://www.mdpi.com/1999-4893/18/6/368
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Synthesizing Explainability Across Multiple ML Models for Structured Data

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