{"id":"W3110889378","doi":"10.1109/smc42975.2020.9283208","title":"Binary Hybrid Differential Evolution Algorithm for Multi-label Feature Selection","year":2020,"lang":"en","type":"article","venue":"","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Feature selection; Computer science; Artificial intelligence; Feature (linguistics); Metaheuristic; Differential evolution; Machine learning; Pattern recognition (psychology); Field (mathematics); Selection (genetic algorithm); Algorithm; Data mining; Mathematics","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":[0.001231558,0.0005850872,0.001065411,0.0009173108,0.0003703783,0.0006684356,0.001338639,0.0009482313,0.00141452],"category_scores_gemma":[0.001988839,0.000317179,0.0007048344,0.0009655089,0.0004340535,0.0006437899,0.0008088902,0.0008315758,0.0002842826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007051065,"about_ca_system_score_gemma":0.0005755792,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002537694,"about_ca_topic_score_gemma":0.001827339,"domain_scores_codex":[0.999396,0.0001809832,0.00003489943,0.00009813058,0.0002353967,0.00005462008],"domain_scores_gemma":[0.9994237,0.0003233547,0.00005310045,0.0000338505,0.0001464409,0.00001953665],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001480214,0.0001213504,0.001840626,0.0001290463,0.0001188193,0.0001381098,0.0001509967,0.6841528,0.01104844,0.01446929,0.002145622,0.2855368],"study_design_scores_gemma":[0.00001033358,0.00002226077,0.0001358534,0.000004122784,0.000005944576,0.00001925181,0.000004193403,0.9971623,0.0006897394,0.00141235,0.0005299257,0.000003722682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01186547,0.000271327,0.986347,0.0001354279,0.00002921949,0.00004111368,0.00002397811,0.0001942448,0.001092177],"genre_scores_gemma":[0.4985288,0.0003006685,0.4956253,0.0002880703,0.00005170537,0.0004565232,0.000252133,0.00008305552,0.004413756],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002537694,"threshold_uncertainty_score":0.006513119,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04131484145864265,"score_gpt":0.2767897758277696,"score_spread":0.2354749343691269,"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."}}