{"id":"W4292236749","doi":"10.1002/cjce.24610","title":"A <scp>multi‐fault</scp> diagnosis method based on improved <scp>SMOTE</scp> for <scp>class‐imbalanced</scp> data","year":2022,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; China Scholarship Council; National Natural Science Foundation of China","keywords":"Mahalanobis distance; Artificial intelligence; Computer science; AdaBoost; Pattern recognition (psychology); Classifier (UML); Oversampling; Decision tree; Machine learning; Data mining; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001027961,0.0005793903,0.0007894385,0.001428248,0.0007210874,0.000702487,0.0008026069,0.0008463903,0.001328034],"category_scores_gemma":[0.002180153,0.0003012473,0.0009817362,0.0007949042,0.0003926133,0.0009790802,0.0007717534,0.001032081,0.0003425396],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000546354,"about_ca_system_score_gemma":0.001264306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007448595,"about_ca_topic_score_gemma":0.005544822,"domain_scores_codex":[0.9992029,0.0000693117,0.00005952235,0.0001842079,0.0003935154,0.00009043036],"domain_scores_gemma":[0.9986978,0.0002800446,0.0001307543,0.0001194314,0.0007043122,0.00006769175],"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":[0.0004268897,0.0002053796,0.008382373,0.0001903778,0.0001173961,0.000494673,0.0001844461,0.2860115,0.04935906,0.005328354,0.006214844,0.6430846],"study_design_scores_gemma":[0.000006155216,0.00001958957,0.0006646074,0.000003212203,0.000005729123,0.00003784249,0.00001275054,0.9949491,0.003369495,0.0004932981,0.0004332531,0.000004952733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04275033,0.0001032673,0.9549804,0.0002443432,0.00006873698,0.00006918952,0.00008333999,0.0008067708,0.0008936152],"genre_scores_gemma":[0.6632542,0.000129962,0.3315437,0.0001926855,0.00009053339,0.0001370679,0.0006778443,0.0001018444,0.003872149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007448595,"threshold_uncertainty_score":0.01481044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347348710776222,"score_gpt":0.2447312378123177,"score_spread":0.2212577507045554,"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."}}