{"id":"W4378942269","doi":"10.48550/arxiv.2305.18352","title":"Multi-Objective Genetic Algorithm for Multi-View Feature Selection","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Jane ja Aatos Erkon Säätiö; Eisai; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; Itä-Suomen Yliopisto; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Interpretability; Computer science; Feature selection; Benchmark (surveying); Feature (linguistics); Artificial intelligence; Machine learning; Selection (genetic algorithm); Data mining; Generalization; Generalizability theory; Genetic algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002687359,0.0007104826,0.000650133,0.0006408462,0.000469829,0.0001997503,0.001791602,0.00066951,0.000009278365],"category_scores_gemma":[0.0001616591,0.0008597993,0.0004493067,0.001719215,0.0001382814,0.0006069691,0.001676755,0.0009647695,0.0001312712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008784509,"about_ca_system_score_gemma":0.0004011678,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000115371,"about_ca_topic_score_gemma":0.0001296419,"domain_scores_codex":[0.995963,0.0002401283,0.0003329759,0.002560783,0.0001668242,0.0007362851],"domain_scores_gemma":[0.9967752,0.0002185337,0.0005328163,0.001143665,0.001053271,0.0002765635],"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":[0.0000193923,0.0002832953,0.0003014207,0.0001157051,0.0002756762,0.000137408,0.000381165,0.9708359,0.00004046429,0.001431674,0.0003052196,0.02587272],"study_design_scores_gemma":[0.001952267,0.00009645023,0.003088454,0.0001113709,0.0001047125,0.00001425768,0.00008617943,0.9905446,0.0002158388,0.002360501,0.0005378256,0.0008875312],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002365544,0.0001704975,0.9942021,0.00007604405,0.001798669,0.002048295,0.0001498281,0.001284938,0.00003300945],"genre_scores_gemma":[0.007107331,0.0005413875,0.9808625,0.0001097905,0.0001998068,0.00004833435,0.00008921487,0.0001201113,0.01092151],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02498518,"threshold_uncertainty_score":0.9993853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09182659539403061,"score_gpt":0.2447307976027236,"score_spread":0.152904202208693,"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."}}