{"id":"W3013276295","doi":"10.1109/access.2020.2983003","title":"SMOTEFUNA: Synthetic Minority Over-Sampling Technique Based on Furthest Neighbour Algorithm","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"European Social Fund; Magyarország Kormánya","keywords":"Computer science; Algorithm; Sampling (signal processing); Detector; Telecommunications","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":[0.001613469,0.0006352267,0.001336714,0.00127949,0.0007854156,0.0006516082,0.001480325,0.0009944893,0.001225398],"category_scores_gemma":[0.004108764,0.0003071126,0.0009175731,0.000719339,0.0004604169,0.0007086233,0.0007832189,0.0009031998,0.0004501098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004686771,"about_ca_system_score_gemma":0.001093601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004952862,"about_ca_topic_score_gemma":0.008004157,"domain_scores_codex":[0.9993005,0.000214453,0.00005048458,0.0001336278,0.0002226439,0.00007830458],"domain_scores_gemma":[0.9986733,0.0006609334,0.0001173073,0.0001397079,0.0003552814,0.00005355084],"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":[0.0006519231,0.0003263388,0.009082205,0.0002283376,0.0002191335,0.0002872267,0.0003417349,0.3954651,0.02019847,0.006952003,0.007157667,0.5590899],"study_design_scores_gemma":[0.00002215779,0.00005802919,0.0004649397,0.000006257615,0.00001272235,0.00005277897,0.00002379098,0.9940857,0.002750698,0.001284692,0.001230455,0.00000768779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08048127,0.0004490388,0.9146027,0.000161953,0.0001641587,0.0002564132,0.0001807897,0.002006595,0.001697088],"genre_scores_gemma":[0.4591708,0.0001634569,0.5363368,0.0002313962,0.00009189514,0.0004470336,0.0009493966,0.0001715415,0.002437653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004952862,"threshold_uncertainty_score":0.009848058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06019594001374554,"score_gpt":0.3177182528903892,"score_spread":0.2575223128766436,"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."}}