{"id":"W2989227906","doi":"10.3233/jifs-182656","title":"Bootstrapping and multiple imputation ensemble approaches for classification problems","year":2019,"lang":"en","type":"article","venue":"Journal of Intelligent & Fuzzy Systems","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Rehabilitation Institute","funders":"","keywords":"Imputation (statistics); Missing data; Bootstrapping (finance); Computer science; Ensemble learning; Statistics; Classifier (UML); Artificial intelligence; Data mining; Econometrics; Machine learning; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008239289,0.001308347,0.002687086,0.002258719,0.0008681402,0.00126789,0.002100124,0.001540289,0.0009631187],"category_scores_gemma":[0.01647722,0.0005114141,0.001837563,0.002535081,0.0005164258,0.00196907,0.001391109,0.002302286,0.0005165201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005286749,"about_ca_system_score_gemma":0.0008866113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002418277,"about_ca_topic_score_gemma":0.002645703,"domain_scores_codex":[0.9952531,0.002635395,0.0002688172,0.0005598127,0.001091455,0.0001914715],"domain_scores_gemma":[0.9890955,0.007101842,0.00066333,0.001174226,0.00175056,0.0002144552],"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":[0.0001738704,0.0001551355,0.004772295,0.0002466419,0.0009507973,0.00014979,0.0001694781,0.6581254,0.00169456,0.009290849,0.00245952,0.3218116],"study_design_scores_gemma":[0.00001043562,0.00006913114,0.0006962065,0.0000302194,0.00005195164,0.00004820327,0.0000243639,0.9841377,0.0006992717,0.0130662,0.001146058,0.00002018005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0108101,0.001335113,0.9865292,0.0001870426,0.00008941288,0.00006216528,0.0000832187,0.0002411327,0.0006625036],"genre_scores_gemma":[0.3790156,0.002231083,0.614975,0.0002972527,0.000537108,0.0004962845,0.0008375986,0.0001096539,0.001500465],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008239289,"threshold_uncertainty_score":0.04357404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08382729232445184,"score_gpt":0.2753863630193097,"score_spread":0.1915590706948579,"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."}}