Synthesizing Explainability Across Multiple ML Models for Structured Data

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...

Full description

Saved in:
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
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items