{"id":"W4394862804","doi":"10.1109/tkde.2024.3388526","title":"Feature Selection With Discernibility and Independence Criteria","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Feature selection; Selection (genetic algorithm); Independence (probability theory); Artificial intelligence; Feature (linguistics); Data mining; Pattern recognition (psychology); Mathematics; Statistics","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":[0.005212975,0.002041048,0.002753546,0.004864319,0.0007944079,0.002499818,0.002042175,0.00151562,0.002587475],"category_scores_gemma":[0.0159655,0.0005968459,0.002500927,0.003954451,0.001113053,0.001765882,0.00205691,0.001712862,0.001121756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00100881,"about_ca_system_score_gemma":0.001952081,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002301498,"about_ca_topic_score_gemma":0.001310395,"domain_scores_codex":[0.9950917,0.001209657,0.0005021123,0.0007711049,0.002079631,0.0003458195],"domain_scores_gemma":[0.9940672,0.003403342,0.0004558714,0.0005112236,0.001370456,0.0001918352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008905089,0.0002420198,0.004036619,0.0004606986,0.0003657481,0.0004690473,0.0001648536,0.2381906,0.01132775,0.02599787,0.008701055,0.7091532],"study_design_scores_gemma":[0.0001311572,0.0002559493,0.001950686,0.00004883146,0.0001055425,0.0002460959,0.00003966723,0.9633288,0.006710499,0.02226556,0.004862952,0.00005422104],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01148511,0.000401589,0.9853092,0.0001643615,0.00004435506,0.0002719783,0.000246213,0.0006377157,0.001439562],"genre_scores_gemma":[0.3506616,0.0006949024,0.6397681,0.000299707,0.0003736992,0.001260867,0.002572274,0.0002592986,0.004109529],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005212975,"threshold_uncertainty_score":0.02756917,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01885862692112788,"score_gpt":0.2740728741851766,"score_spread":0.2552142472640487,"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."}}