{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003877281,0.0006123902,0.0006081859,0.0005068318,0.0005639697,0.0003151805,0.001308819,0.0005638828,0.00001632618],"category_scores_gemma":[0.0004172614,0.0005911195,0.0003492027,0.000879508,0.00006355561,0.0005433219,0.001518169,0.000959055,0.00007212198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006088451,"about_ca_system_score_gemma":0.0003650351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001224076,"about_ca_topic_score_gemma":0.0001805811,"domain_scores_codex":[0.9967402,0.0001306671,0.0003921392,0.001699396,0.0004143336,0.0006232772],"domain_scores_gemma":[0.9971405,0.0002908596,0.0004200027,0.0008949348,0.001065015,0.0001886953],"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":[0.00007450463,0.0006038109,0.000456208,0.0002230271,0.000546208,0.00001421742,0.003080571,0.9364859,0.00039781,0.01203771,0.03060366,0.0154764],"study_design_scores_gemma":[0.001105646,0.0001439262,0.002831509,0.00007064971,0.00002531217,0.00001235349,0.00006339835,0.9863873,0.002137555,0.004468454,0.002046531,0.0007073825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00005006732,0.00005742709,0.9903348,0.001430623,0.002797431,0.002242628,0.0001020737,0.002433182,0.0005517873],"genre_scores_gemma":[0.003948004,0.00004255649,0.9652185,0.0002815568,0.0003412659,0.000367891,0.000158881,0.0001087887,0.02953253],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04990141,"threshold_uncertainty_score":0.999654,"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."}}