{"id":"W3162853854","doi":"10.32604/cmc.2021.014840","title":"A New Hybrid Feature Selection Method Using T-test and Fitness Function","year":2021,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature selection; Selection (genetic algorithm); Test (biology); Fitness function; Function (biology); Computer science; Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Machine learning; Biology; Genetic algorithm; Evolutionary biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication"],"consensus_categories":["metaepi_narrow"],"category_scores_codex":[0.001642983,0.001305789,0.002036725,0.0005299925,0.0006761188,0.004961479,0.001430098,0.0005843035,0.0007443813],"category_scores_gemma":[0.0002207123,0.001357678,0.0003027894,0.0007811857,0.0001285639,0.002382179,0.00227215,0.0004421562,0.0002587381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002259953,"about_ca_system_score_gemma":0.0004289555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004557145,"about_ca_topic_score_gemma":0.0000346086,"domain_scores_codex":[0.9919313,0.001352767,0.001733958,0.002675155,0.0008229461,0.001483841],"domain_scores_gemma":[0.995213,0.0006914446,0.001183694,0.001442144,0.0008746414,0.0005950526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001957798,0.000177005,0.0001801017,0.000275874,0.0002106022,0.0002128712,0.0004905955,0.00007166196,0.9194081,0.001031326,0.03289732,0.04484879],"study_design_scores_gemma":[0.003720819,0.0003746806,0.00467182,0.001400138,0.0002516136,0.001577132,0.00007586653,0.0066762,0.9490427,0.002233481,0.02804591,0.001929663],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.3892279,0.0002886523,0.596353,0.0009523086,0.01132325,0.0008284975,0.0001002819,0.0008486729,0.00007740914],"genre_scores_gemma":[0.3650335,0.0001938996,0.625,0.002503335,0.004743313,0.000104834,0.0004858853,0.0002402006,0.001695056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04291913,"threshold_uncertainty_score":0.9999694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01365653497227175,"score_gpt":0.2486228614086436,"score_spread":0.2349663264363718,"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."}}