{"id":"W4391123134","doi":"10.1016/j.engappai.2024.107934","title":"An improved generative adversarial network to oversample imbalanced datasets","year":2024,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Key Research and Development Program of China; National University's Basic Research Foundation of China; Fundamental Research Funds for the Central Universities; Ministry of Science and Technology of the People's Republic of China; National Natural Science Foundation of China","keywords":"Discriminator; Computer science; Oversampling; Benchmark (surveying); Residual; Artificial intelligence; Pattern recognition (psychology); Constraint (computer-aided design); Sampling (signal processing); Algorithm; Data mining; Machine learning; Detector; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003335041,0.0001665239,0.0001639392,0.0001886539,0.00008596922,0.000212259,0.001272883,0.00006868372,0.00001543968],"category_scores_gemma":[0.00006536821,0.0001817561,0.00004625614,0.00129398,0.00003670192,0.0005811702,0.000168707,0.0001573502,0.00008331538],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007223797,"about_ca_system_score_gemma":0.00008031874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000457253,"about_ca_topic_score_gemma":0.000007715642,"domain_scores_codex":[0.9985062,0.00002171101,0.0004287886,0.0005770016,0.0001861083,0.000280126],"domain_scores_gemma":[0.9983307,0.000142666,0.00006032466,0.001237771,0.00009570379,0.0001329098],"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":[0.000006502265,0.00005990107,0.00000264787,0.00002845027,0.00002074337,0.000001053008,0.0003037263,0.07187807,0.1356448,0.6466141,0.001665951,0.1437741],"study_design_scores_gemma":[0.000007564594,0.00006840516,0.00002178016,0.00002566437,0.000005719519,0.000001772902,0.00001788852,0.7747689,0.1904823,0.007575443,0.02682693,0.0001976211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001440065,0.0000840592,0.9974877,0.0003415306,0.0003135961,0.0005456599,0.0002478842,0.0007994837,0.00003614081],"genre_scores_gemma":[0.3713622,0.00001320855,0.6276031,0.00007556658,0.0003132257,0.0004064762,0.0001988071,0.00001849413,0.00000896095],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7028908,"threshold_uncertainty_score":0.7411799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01907279863279813,"score_gpt":0.2998789503065539,"score_spread":0.2808061516737557,"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."}}