{"id":"W4416145973","doi":"10.1016/j.ijar.2025.109590","title":"Multi-granularity Knowledge Fusion for Feature Selection Using Granular-ball Entropy Uncertainty Measures","year":2025,"lang":"en","type":"article","venue":"International Journal of Approximate Reasoning","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta; University of Regina","funders":"National Key Research and Development Program of China; Key Technologies Research and Development Program; National Natural Science Foundation of China","keywords":"Feature selection; Entropy (arrow of time); Granularity; Knowledge space; Feature (linguistics); Granular computing; Mutual information; Adaptability; Knowledge extraction; Measure (data warehouse)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009338685,0.0001831306,0.0002836571,0.0003551955,0.0002245231,0.0003696829,0.001002638,0.000122477,0.00000256611],"category_scores_gemma":[0.0002889202,0.0001493292,0.000260508,0.0003179974,0.00003523087,0.0005067561,0.0001697227,0.000313342,8.88866e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000345016,"about_ca_system_score_gemma":0.0002517535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004446609,"about_ca_topic_score_gemma":0.00001805238,"domain_scores_codex":[0.9984928,0.0001017782,0.0004378527,0.0002672739,0.0004427221,0.0002576039],"domain_scores_gemma":[0.9979179,0.0001141462,0.0004319403,0.0001454448,0.001313069,0.00007749509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002035943,0.002862256,0.03426719,0.0003457691,0.002097409,0.0002211249,0.004675915,0.06382436,0.1575829,0.3168809,0.004895278,0.4103109],"study_design_scores_gemma":[0.00199488,0.0000921185,0.003049422,0.0004175859,0.00005178748,0.0002110167,0.00005276118,0.9782246,0.003504255,0.006627941,0.005577418,0.0001961716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03637023,0.000871604,0.9595745,0.0007666916,0.001867329,0.0001985066,0.00000880645,0.00004723022,0.0002950797],"genre_scores_gemma":[0.4964699,0.00009228843,0.5029122,0.0001362396,0.0003114161,0.000005090012,0.000008500029,0.00001026397,0.00005413449],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9144003,"threshold_uncertainty_score":0.6089467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02751658879673127,"score_gpt":0.3168614214051067,"score_spread":0.2893448326083754,"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."}}