{"id":"W4391306046","doi":"10.1109/smc53992.2023.10394458","title":"Compact NSGA-II for Multi-objective Feature Selection","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Brock University; Ontario Tech University","funders":"","keywords":"Feature selection; Computer science; Metric (unit); Artificial intelligence; Selection (genetic algorithm); Evolutionary algorithm; Binary classification; Feature (linguistics); Population; Binary number; Task (project management); Data mining; Machine learning; Feature vector; Optimization problem; Compact space; Multi-objective optimization; Pattern recognition (psychology); Algorithm; Mathematics; Support vector machine; Engineering","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":[0.001252201,0.001700585,0.001162907,0.001004648,0.00042653,0.0008191945,0.001288821,0.001107604,0.003026951],"category_scores_gemma":[0.003397692,0.0005150371,0.000931497,0.0009443505,0.000603758,0.0006272228,0.0009184722,0.001238202,0.0004311697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00094331,"about_ca_system_score_gemma":0.001483388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004857763,"about_ca_topic_score_gemma":0.004667789,"domain_scores_codex":[0.9993318,0.0003087104,0.00004530741,0.00007456462,0.0001760761,0.00006353058],"domain_scores_gemma":[0.9987961,0.0007323557,0.0001272509,0.00008093141,0.0002232057,0.00004011258],"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":[0.00004205158,0.00003440014,0.0003676005,0.00008821978,0.00003852486,0.00005293346,0.00004470774,0.9690196,0.001114339,0.004071564,0.0008733206,0.02425267],"study_design_scores_gemma":[0.00001885873,0.00003122916,0.000124098,0.00001188124,0.000008093421,0.0000138058,0.000009878312,0.9969763,0.0003836506,0.001600063,0.0008176166,0.000004572682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04272459,0.000501032,0.9507486,0.0002247015,0.00008986975,0.0003277923,0.0001951386,0.0006142601,0.004574023],"genre_scores_gemma":[0.5289813,0.0004546028,0.462083,0.0002256333,0.00004665487,0.002270334,0.0007691365,0.0001507274,0.005018558],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004857763,"threshold_uncertainty_score":0.01012617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06181858722780776,"score_gpt":0.3416014521611343,"score_spread":0.2797828649333265,"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."}}