{"id":"W4285287188","doi":"10.2139/ssrn.4136029","title":"2-Stage Swarm Search Feature Selection for Classification of High Dimension Bioinformatics Dataset","year":2022,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"","keywords":"Feature selection; Selection (genetic algorithm); Stage (stratigraphy); Artificial intelligence; Pattern recognition (psychology); Computer science; Swarm behaviour; Feature (linguistics); Data mining; Machine learning; Computational biology; Biology","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":[0.001623813,0.0001258501,0.0001334102,0.00008818514,0.0003988942,0.00002292451,0.0002845682,0.00008723318,0.00001904856],"category_scores_gemma":[0.00009033016,0.0001200912,0.00008063712,0.0001630097,0.00003304016,0.00001419895,0.0001314371,0.001055155,0.000002326838],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002527161,"about_ca_system_score_gemma":0.0009009756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002421124,"about_ca_topic_score_gemma":0.00006894517,"domain_scores_codex":[0.9983673,0.00009683921,0.0003184463,0.0001321744,0.0002953994,0.0007898588],"domain_scores_gemma":[0.9992571,0.00002059342,0.000326825,0.0002242029,0.0001249612,0.00004629195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003534021,0.0008376828,0.01191772,0.0006227998,0.001407137,0.000001231931,0.001154912,0.0920259,0.6345946,0.1145884,0.08204827,0.05726737],"study_design_scores_gemma":[0.008254665,0.01520607,0.003548031,0.00004108907,0.0003098168,0.002146028,0.01295036,0.297399,0.09342273,0.008207283,0.5570133,0.001501514],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8442615,0.0004835397,0.1525264,0.0008854298,0.0002757063,0.0005527937,0.0008338588,0.00001927587,0.0001614418],"genre_scores_gemma":[0.9869089,0.0004084618,0.005339264,0.000130646,0.0001599176,0.00002233846,0.00550832,0.00002690467,0.001495244],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5411718,"threshold_uncertainty_score":0.4897177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009869589778197062,"score_gpt":0.2760159001053437,"score_spread":0.2661463103271466,"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."}}