{"id":"W2126489459","doi":"10.1016/j.inffus.2008.11.003","title":"Overfitting cautious selection of classifier ensembles with genetic algorithms","year":2008,"lang":"en","type":"article","venue":"Information Fusion","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Overfitting; Computer science; Artificial intelligence; Machine learning; Classifier (UML); Ensemble learning; Selection (genetic algorithm); Random subspace method; Data mining; Pattern recognition (psychology); Artificial neural network","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":[0.006401018,0.001211141,0.001937916,0.00194121,0.001401669,0.00185449,0.001620538,0.001928824,0.001346057],"category_scores_gemma":[0.01612848,0.0006517858,0.001339767,0.001168461,0.0009070673,0.002022572,0.001576006,0.001902066,0.0005278908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001051625,"about_ca_system_score_gemma":0.001500574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004619428,"about_ca_topic_score_gemma":0.005351166,"domain_scores_codex":[0.9973501,0.001064233,0.0001515925,0.0003977395,0.0008077109,0.0002286016],"domain_scores_gemma":[0.9957094,0.001881398,0.000291695,0.000548919,0.001446924,0.0001216764],"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":[0.0002952723,0.0001583651,0.003515001,0.00007000268,0.0003136148,0.0001386237,0.0002750682,0.6784233,0.008144648,0.01083026,0.0020916,0.2957442],"study_design_scores_gemma":[0.00001019819,0.0000332638,0.0002640464,0.00001070693,0.00003432003,0.00003446464,0.0000207996,0.9927054,0.002061999,0.004460216,0.0003558253,0.000008791723],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03893035,0.00018756,0.9583065,0.0001993997,0.00006735676,0.00006951033,0.00002165236,0.0005033936,0.001714226],"genre_scores_gemma":[0.543211,0.000130981,0.4533001,0.0002926188,0.00007388048,0.0001756251,0.0001295566,0.0002041005,0.002482109],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006401018,"threshold_uncertainty_score":0.03385222,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01127499382037043,"score_gpt":0.2182655320542314,"score_spread":0.206990538233861,"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."}}