{"schema":"pith.reference-change-event.v1","doi":"10.1007/s11831-025-10309-5","canonical_url":"https://pith.science/event/10.1007/s11831-025-10309-5","json_url":"https://pith.science/event/10.1007/s11831-025-10309-5.json","not_a_judgment":"This page records that a citing paper's bibliography includes a work with a published notice. It is not a judgment on the citing paper.","primary":{"event_id":298860,"doi":"10.1007/s11831-025-10309-5","event_type":"correction","event_type_label":"Correction","source":"crossref","source_label":"Crossref","event_date":"2026-01-13","title":"Correction: Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review","work_title":"Archives of Computational Methods in Engineering , author =","work_doi":"10.1007/s11831-025-10309-5","work_arxiv_id":null,"notice_doi":"10.1007/s11831-025-10491-6","flag_count":0,"flags_open":0,"flags_disputed":0,"latest_flag_at":null,"human_href":"/event/10.1007/s11831-025-10309-5","json_href":"/event/10.1007/s11831-025-10309-5.json"},"events":[{"event_id":298860,"doi":"10.1007/s11831-025-10309-5","event_type":"correction","event_type_label":"Correction","source":"crossref","source_label":"Crossref","event_date":"2026-01-13","title":"Correction: Advancements in Hybrid Machine Learning Models for Biomedical Disease Classification Using Integration of Hyperparameter-Tuning and Feature Selection Methodologies: A Comprehensive Review","work_title":"Archives of Computational Methods in Engineering , author =","work_doi":"10.1007/s11831-025-10309-5","work_arxiv_id":null,"notice_doi":"10.1007/s11831-025-10491-6","flag_count":0,"flags_open":0,"flags_disputed":0,"latest_flag_at":null,"human_href":"/event/10.1007/s11831-025-10309-5","json_href":"/event/10.1007/s11831-025-10309-5.json"}],"flags":[{"id":10250,"status":"open","status_label":"Open","citing_arxiv_id":"2608.13108","citing_title":"Robust Dempster-Shafer Evidence Fusion with Chaos-Conflict Measurement and Historical-Experience Weighting","ref_index":20,"evidence_raw":"Advancements in hybrid machine learning models for biomedical disease classification using integration of hyperparameter-tuning and feature selection methodologies: a comprehensive review , volume =. Archives of Computational Methods in Engineering , author =. 2026 , pages =. doi:10.1007/s11831-025-10309-5 , language =","evidence_cleaned":"Advancements in hybrid machine learning models for biomedical disease classification using integration of hyperparameter-tuning and feature selection methodologies: a comprehensive review, volume =. Archives of Computational Methods in Engineering, author =. 2026, pages =. doi:10.1007/s11831-025-10309-5, language =","evidence_source_label":"bibliography line","event_type":"correction","event_type_label":"Correction","source_label":"Crossref","event_date":"2026-01-13","work_title":"Archives of Computational Methods in Engineering , author =","work_doi":"10.1007/s11831-025-10309-5","event_doi":"10.1007/s11831-025-10309-5","flag_href":"/flags/10250","event_href":"/event/10.1007/s11831-025-10309-5","paper_href":"/paper/2608.13108","created_at":"2026-08-15T18:17:58.496781Z","dispute_note":null,"disputed_at":null,"disputed_by":null}],"flag_count":1,"flags_open":1,"flags_disputed":0,"desk_url":"https://pith.science/flags"}