{"id":"W2016120275","doi":"10.1109/tevc.2013.2292181","title":"Guest Editorial: Special Issue on Advances in Multiobjective Evolutionary Algorithms for Data Mining","year":2014,"lang":"en","type":"editorial","venue":"IEEE Transactions on Evolutionary Computation","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Evolutionary algorithm; Snapshot (computer storage); Computer science; Evolutionary computation; Data science; Domain (mathematical analysis); Multi-objective optimization; Data mining; Machine learning; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008218749,0.0007097812,0.000701967,0.0009351815,0.0008242454,0.000229924,0.002242124,0.0008992507,0.0000126432],"category_scores_gemma":[0.0002412241,0.0008148865,0.0002025762,0.001005221,0.0001651974,0.002153243,0.00004420935,0.00118505,0.0001911774],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009522512,"about_ca_system_score_gemma":0.0008167782,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001300594,"about_ca_topic_score_gemma":0.0001209223,"domain_scores_codex":[0.9941424,0.000280135,0.001031412,0.002262998,0.00162425,0.0006588539],"domain_scores_gemma":[0.9930586,0.003838122,0.0005678124,0.001560677,0.0007921712,0.0001826797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001054619,0.0005494712,3.702974e-7,0.00005642073,0.00004130725,0.000002616125,0.0001500023,0.04137588,0.00000160547,0.00006081149,0.8198394,0.1378166],"study_design_scores_gemma":[0.001021847,0.0004043664,0.00001375755,0.0001990782,0.00003714243,0.000003038611,0.00003096074,0.4157942,0.000008563105,0.0005397873,0.5814624,0.0004848573],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"editorial","genre_scores_codex":[7.394114e-7,0.0001048662,0.5039654,0.0001648381,0.4921254,0.000672062,0.002572678,0.0001971579,0.0001968828],"genre_scores_gemma":[0.00006977963,0.0002454356,0.1663375,0.00004427996,0.8269699,0.0006204682,0.005291525,0.00007795578,0.0003432165],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.3744183,"threshold_uncertainty_score":0.9994302,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02128598396408002,"score_gpt":0.3156270003797391,"score_spread":0.2943410164156591,"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."}}