{"id":"W7117109126","doi":"10.1109/tpami.2025.3647921","title":"Robust Semi-Supervised Feature Selection With Multi-Granularity Zentropy Modeling","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Robustness (evolution); Feature selection; Granularity; Pattern recognition (psychology); Data modeling; Feature learning; Feature (linguistics); Fuzzy logic","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002085571,0.0009969547,0.001794684,0.001153309,0.0006634962,0.001283755,0.002063041,0.001086429,0.0006992172],"category_scores_gemma":[0.00517716,0.0004297049,0.001442864,0.001122222,0.001092938,0.00190189,0.001760864,0.001431628,0.000287148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008107751,"about_ca_system_score_gemma":0.001165557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002106878,"about_ca_topic_score_gemma":0.002131315,"domain_scores_codex":[0.9977411,0.0006091619,0.0001476074,0.0006191491,0.0007114689,0.0001715523],"domain_scores_gemma":[0.9978502,0.0009503774,0.0003311313,0.0003567598,0.0004219966,0.0000894972],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004401703,0.0001964372,0.005342307,0.0002546028,0.000245497,0.0003118832,0.0003583132,0.5660293,0.01548741,0.01612561,0.00426823,0.3909402],"study_design_scores_gemma":[0.00001044407,0.00002770051,0.0003822701,0.000006173067,0.00001107714,0.00003392799,0.0000174571,0.9919218,0.001625933,0.005599423,0.0003531694,0.00001067543],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0209837,0.0001693891,0.9778069,0.0001182284,0.00001498446,0.00003991209,0.00006229607,0.0004553883,0.0003491954],"genre_scores_gemma":[0.7505426,0.0002205851,0.2461201,0.0002699917,0.0001288591,0.0002677294,0.0008100751,0.0001218349,0.001518139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002106878,"threshold_uncertainty_score":0.01102966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02749125216196364,"score_gpt":0.2669507304775671,"score_spread":0.2394594783156034,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}