{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,19]],"date-time":"2026-01-19T10:54:16Z","timestamp":1768820056744,"version":"3.49.0"},"reference-count":21,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institutes of Health\/National Institute of Mental Health (NIH\/NIMH)","award":["NCT03452813"],"award-info":[{"award-number":["NCT03452813"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>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 and consistency by aggregating ranked outputs from diverse explainers. WISFC assigns a weighted score to each feature based on its rank and frequency across model-explainer pairs, providing a robust ensemble feature-importance ranking. Unlike simple consensus voting or ranking heuristics that are insufficient for capturing complex relationships among different explainer outputs, WISFC offers a more principled approach to reconciling and aggregating this information. By aggregating many \u201cweak signals\u201d from brute-force modeling runs, WISFC can surface a stronger consensus on which variables matter most. The framework is designed to be reproducible and generalizable, capable of taking important outputs from any set of machine-learning models and producing an aggregated ranking highlighting consistently important features. This approach acknowledges that any single model is a simplification of complex, multidimensional phenomena; using multiple diverse models, each optimized from a different perspective, WISFC systematically captures different facets of the problem space to create a more structured and comprehensive view. As a consequence, this study offers a useful strategy for researchers and practitioners who seek innovative ways of exploring complex systems, not by discovering entirely new variables but by introducing a novel mindset for systematically combining multiple modeling perspectives.<\/jats:p>","DOI":"10.3390\/a18060368","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:08:06Z","timestamp":1750219686000},"page":"368","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Synthesizing Explainability Across Multiple ML Models for Structured Data"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3831-5433","authenticated-orcid":false,"given":"Emir","family":"Veledar","sequence":"first","affiliation":[{"name":"Department of Neurology, University of Miami Miller School of Medicine, 1120 NW 14th Street, Suite 1370, Miami, FL 33136, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9684-5993","authenticated-orcid":false,"given":"Lili","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Miami Miller School of Medicine, 1120 NW 14th Street, Suite 1370, Miami, FL 33136, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8302-7721","authenticated-orcid":false,"given":"Omar","family":"Veledar","sequence":"additional","affiliation":[{"name":"Beevadoo e.U., Pfeifferhofweg 3b, 8045 Graz, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1416-632X","authenticated-orcid":false,"given":"Hannah","family":"Gardener","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Miami Miller School of Medicine, 1120 NW 14th Street, Suite 1370, Miami, FL 33136, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9879-3912","authenticated-orcid":false,"given":"Carolina M.","family":"Gutierrez","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Miami Miller School of Medicine, 1120 NW 14th Street, Suite 1370, Miami, FL 33136, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3667-2792","authenticated-orcid":false,"given":"Jose G.","family":"Romano","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Miami Miller School of Medicine, 1120 NW 14th Street, Suite 1370, Miami, FL 33136, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7115-9815","authenticated-orcid":false,"given":"Tatjana","family":"Rundek","sequence":"additional","affiliation":[{"name":"Department of Neurology, University of Miami Miller School of Medicine, 1120 NW 14th Street, Suite 1370, Miami, FL 33136, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","first-page":"1759","article-title":"XAI-based cross-ensemble feature ranking methodology for machine learning models","volume":"15","author":"Jiang","year":"2023","journal-title":"Int. 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