{"id":"W2949679216","doi":"10.1016/j.knosys.2019.06.013","title":"Similarity-learning information-fusion schemes for missing data imputation","year":2019,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Imputation (statistics); Missing data; Categorical variable; Computer science; Fusion mechanism; Data mining; Sensor fusion; Artificial intelligence; Fusion; Pattern recognition (psychology); Machine learning","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":[0.01450687,0.00085851,0.003130509,0.002254855,0.001825979,0.003111311,0.004940588,0.003151023,0.002013829],"category_scores_gemma":[0.03371989,0.0007464485,0.002102536,0.005539408,0.002603529,0.008533278,0.006915418,0.003979597,0.0009700975],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002018411,"about_ca_system_score_gemma":0.002862953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001215256,"about_ca_topic_score_gemma":0.0009103114,"domain_scores_codex":[0.9870967,0.005983972,0.0009215262,0.001775645,0.003482693,0.0007395483],"domain_scores_gemma":[0.9766024,0.01035691,0.001605218,0.008701311,0.002298608,0.0004355784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00110459,0.0003936162,0.001825458,0.0003846858,0.0004140567,0.0002094796,0.0006041098,0.2505113,0.004910271,0.3840218,0.006085861,0.3495347],"study_design_scores_gemma":[0.00004357344,0.0001645731,0.0003351955,0.00004725371,0.000069489,0.0001932291,0.00006657229,0.7076043,0.004894161,0.2842576,0.002267002,0.00005708344],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00424483,0.0003051142,0.9945432,0.0002194156,0.00003065631,0.0000351965,0.00008155695,0.0001291862,0.0004108243],"genre_scores_gemma":[0.5150574,0.0009020269,0.479262,0.0003718038,0.0002652698,0.0002146877,0.0007761052,0.00008427504,0.003066486],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01450687,"threshold_uncertainty_score":0.0767206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04171441885615958,"score_gpt":0.302640101526308,"score_spread":0.2609256826701484,"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."}}