{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001674049,0.001455274,0.001531548,0.001466009,0.0004669237,0.0007757243,0.001324964,0.001531495,0.001206335],"category_scores_gemma":[0.003617857,0.0005421457,0.001324791,0.001423495,0.0006598045,0.000749107,0.0008522458,0.001570444,0.0002706946],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009614052,"about_ca_system_score_gemma":0.001540318,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005857797,"about_ca_topic_score_gemma":0.004599342,"domain_scores_codex":[0.9993312,0.0002861277,0.00003160288,0.0001365858,0.0001535585,0.00006093108],"domain_scores_gemma":[0.998879,0.0007439919,0.00007665187,0.00005149561,0.0002132665,0.00003555144],"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":[0.00004389949,0.00006255725,0.0009221332,0.00004741997,0.00009849758,0.00006818676,0.00005334663,0.9105796,0.001732723,0.005116576,0.001086216,0.08018894],"study_design_scores_gemma":[0.000007856416,0.0000144337,0.0000713655,0.000003713983,0.000006481578,0.000008628635,0.000004500439,0.9979089,0.0002201804,0.001565796,0.0001849757,0.000003155921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01073245,0.0002502868,0.9879574,0.00011242,0.00002707093,0.00005046194,0.00003248921,0.0002062769,0.000631023],"genre_scores_gemma":[0.2994297,0.0002795712,0.6976748,0.0002295395,0.00004905179,0.0004837684,0.000344918,0.00009398594,0.001414752],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005857797,"threshold_uncertainty_score":0.0116474,"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."}}