{"id":"W4379374897","doi":"10.1007/s10489-023-04661-x","title":"Explainable global error weighted on feature importance: The xGEWFI metric to evaluate the error of data imputation and data augmentation","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Outlier; Metric (unit); Imputation (statistics); Data mining; Feature (linguistics); Weighting; Test data; Random forest; Pattern recognition (psychology); Context (archaeology); Algorithm; Artificial intelligence; Statistics; Missing data; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001098842,0.000138845,0.000122615,0.0000950743,0.0003322635,0.0001204083,0.002986594,0.00005376435,0.00001319561],"category_scores_gemma":[0.00006097819,0.00008637868,0.00001836912,0.003015041,0.00007833357,0.0003455561,0.001362244,0.000134928,0.00007644581],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000046852,"about_ca_system_score_gemma":0.00005605981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006044329,"about_ca_topic_score_gemma":0.00003849831,"domain_scores_codex":[0.9983922,0.00004673269,0.0002780027,0.0006708388,0.0004015239,0.0002106945],"domain_scores_gemma":[0.9971337,0.0002118993,0.0001453822,0.002367727,0.00008649202,0.0000548055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005065938,0.00007305959,0.0002065423,0.00002897424,0.00005413249,0.000002190764,0.0008598205,0.001094449,0.001283207,0.3516247,0.04489201,0.5998303],"study_design_scores_gemma":[0.0002119102,0.0002787344,0.0102351,0.00003637359,0.00008698296,0.00002117666,0.002656648,0.8275506,0.03712872,0.09818346,0.0231221,0.0004882218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01162395,0.0001069725,0.9799924,0.005668401,0.00008986922,0.001144672,0.0001228721,0.0002430288,0.001007764],"genre_scores_gemma":[0.9707338,0.0001141402,0.02792423,0.0006747519,0.00004203747,0.0002076751,0.0001785181,0.000009174475,0.0001156247],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9591099,"threshold_uncertainty_score":0.5549884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09666439399236094,"score_gpt":0.389517753719429,"score_spread":0.2928533597270681,"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."}}