{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T08:55:26Z","timestamp":1776416126072,"version":"3.51.2"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T00:00:00Z","timestamp":1775692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2021ZD0201302"],"award-info":[{"award-number":["2021ZD0201302"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Partial multi-label feature selection aims to identify discriminative features from data where each instance is associated with an ambiguous candidate label set. Existing methods are typically built upon single-scale modeling assumptions and may fail to fully exploit the multi-granularity structure underlying instance\u2013label relationships. To address this limitation, we propose a novel framework termed PML-FSMNG, which integrates entropy-weighted multi-scale neighborhood granules with label distribution learning. Specifically, multi-scale neighborhood systems are constructed to estimate label distinguishability at multiple structural scales, and Shannon entropy is employed to adaptively fuse scale-specific label distributions into a robust soft supervisory signal. Based on the learned label distribution, an embedded sparse regression model with \u21132,1-norm regularization is developed for discriminative feature selection, together with an entropy-regularized adaptive graph learning mechanism to preserve intrinsic geometric structure. Extensive experiments on benchmark datasets demonstrate that the proposed method consistently outperforms several state-of-the-art approaches, validating the effectiveness of multi-scale modeling and entropy-guided adaptive learning under label ambiguity.<\/jats:p>","DOI":"10.3390\/e28040422","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T07:55:12Z","timestamp":1775807712000},"page":"422","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Partial Multi-Label Feature Selection via Entropy-Weighted Multi-Scale Neighborhood Granular Label Distribution Learning"],"prefix":"10.3390","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6194-7654","authenticated-orcid":false,"given":"Yifan","family":"Cao","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beihang University, Beijing 100191, China"},{"name":"LMIB and SKLCCSE, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9562-0239","authenticated-orcid":false,"given":"Mao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beihang University, Beijing 100191, China"},{"name":"LMIB and SKLCCSE, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7823-9726","authenticated-orcid":false,"given":"Cong","family":"Wang","sequence":"additional","affiliation":[{"name":"LMIB and SKLCCSE, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-3681-796X","authenticated-orcid":false,"given":"Shuyu","family":"Fan","sequence":"additional","affiliation":[{"name":"LMIB and SKLCCSE, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4340-5414","authenticated-orcid":false,"given":"Ziqiao","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beihang University, Beijing 100191, China"},{"name":"LMIB and SKLCCSE, Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beijing 100083, China"},{"name":"Hangzhou Internation Innovation Institute of Beihang University, Hangzhou 311115, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0540-3779","authenticated-orcid":false,"given":"Binghui","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Beihang University, Beijing 100191, China"},{"name":"LMIB and SKLCCSE, Beihang University, Beijing 100191, China"},{"name":"Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing, Beijing 100083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Lin, J., Su, Q., Yang, P., Ma, S., and Sun, X. 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