{"id":"W7117472703","doi":"10.1016/j.patcog.2025.113009","title":"Average weight margin-based feature selection with three-way decision","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Feature (linguistics); Pattern recognition (psychology); Feature selection; Benchmark (surveying); Margin (machine learning); Measure (data warehouse); Sample (material); Minimum redundancy feature selection","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":[0.002369391,0.001441509,0.004065216,0.001819968,0.001070476,0.001899172,0.002147522,0.0013626,0.002990677],"category_scores_gemma":[0.003525574,0.000658889,0.002744171,0.001723542,0.0006382929,0.001669722,0.001575451,0.001218149,0.0009022471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005984255,"about_ca_system_score_gemma":0.001293918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003143193,"about_ca_topic_score_gemma":0.002590728,"domain_scores_codex":[0.9979916,0.000387361,0.0002153707,0.0005309822,0.0006344705,0.0002403205],"domain_scores_gemma":[0.9986055,0.0006588866,0.00007277428,0.0001201673,0.000476984,0.00006572207],"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":[0.001396482,0.000356102,0.001661896,0.0002431322,0.0004133244,0.0001633669,0.0001639634,0.08957345,0.01470292,0.003062407,0.005571507,0.8826914],"study_design_scores_gemma":[0.00005515815,0.0002288102,0.0009863711,0.00001621684,0.0001208759,0.00008114972,0.00002999037,0.9899755,0.005241199,0.002329243,0.0009000226,0.00003547045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02350667,0.0004460033,0.9740346,0.00009184854,0.0001005532,0.00008398848,0.0000812917,0.0008522397,0.0008028309],"genre_scores_gemma":[0.5290492,0.0002800688,0.4660222,0.0001898182,0.0001173795,0.000338681,0.0007185497,0.0002187997,0.003065391],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004065216,"threshold_uncertainty_score":0.01253068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01092951736863423,"score_gpt":0.218293766461635,"score_spread":0.2073642490930007,"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."}}