{"id":"W2972580158","doi":"10.1002/int.22120","title":"Synthetic minority oversampling for function approximation problems","year":2019,"lang":"en","type":"article","venue":"International Journal of Intelligent Systems","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Oversampling; Categorical variable; Computer science; Artificial intelligence; Machine learning; Function approximation; Preprocessor; Benchmark (surveying); Function (biology); Data mining; Mathematics; Algorithm; Artificial neural network","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":[0.003668664,0.0008512487,0.001047514,0.0009068561,0.0005977483,0.0007892114,0.001005301,0.0008364035,0.0007893938],"category_scores_gemma":[0.009986219,0.0002531771,0.000741643,0.0006692159,0.0006957726,0.001001311,0.001225247,0.001307579,0.0003097641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000683266,"about_ca_system_score_gemma":0.0006312961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001620767,"about_ca_topic_score_gemma":0.001806269,"domain_scores_codex":[0.9987458,0.0005539221,0.00006242889,0.0001952795,0.0003593559,0.00008323864],"domain_scores_gemma":[0.9967079,0.001867778,0.0002938365,0.0005021155,0.0005024556,0.0001258505],"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":[0.0007117057,0.0003605789,0.009532412,0.0002765791,0.0001433142,0.0002341251,0.0002869357,0.6424452,0.01097192,0.02435959,0.008760381,0.3019173],"study_design_scores_gemma":[0.00001071644,0.00004883836,0.0004450438,0.000009664009,0.000006143884,0.00003582074,0.00001834981,0.9906578,0.002058802,0.005640545,0.001064307,0.000004047064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06908451,0.0008444212,0.9269844,0.0004600183,0.0001386928,0.0001166072,0.0001836355,0.0006393245,0.001548424],"genre_scores_gemma":[0.7375548,0.0004358918,0.2578114,0.0003595122,0.0002533517,0.0002793472,0.001293504,0.0001029928,0.001909095],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003668664,"threshold_uncertainty_score":0.01940197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03005787058750423,"score_gpt":0.2787268652002383,"score_spread":0.248668994612734,"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."}}