{"id":"W4378418328","doi":"10.18280/ria.370229","title":"A Study on Imbalanced Data Classification for Various Applications","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Pattern recognition (psychology)","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":[0.01178587,0.001111554,0.001428435,0.005102192,0.002009513,0.00457165,0.001651726,0.001429111,0.001493076],"category_scores_gemma":[0.04749222,0.0004472834,0.001427705,0.008153391,0.001851089,0.007619057,0.001475172,0.003076715,0.0005121168],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002903573,"about_ca_system_score_gemma":0.001506633,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002692713,"about_ca_topic_score_gemma":0.001540126,"domain_scores_codex":[0.9879723,0.003873446,0.0009358121,0.002331902,0.004334714,0.00055176],"domain_scores_gemma":[0.9589425,0.02733232,0.002597603,0.003131597,0.007258274,0.0007377812],"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":[0.0009481035,0.0006263167,0.04980367,0.001446043,0.0003952662,0.0006777577,0.001261905,0.06909712,0.002921895,0.1348582,0.03106622,0.7068975],"study_design_scores_gemma":[0.00004548421,0.0005332487,0.02309,0.0005244422,0.0001919941,0.001230999,0.00152258,0.7616715,0.004630637,0.1538928,0.05255481,0.0001114823],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1438482,0.05638886,0.7597423,0.01790502,0.0037021,0.0006351296,0.00145565,0.0006722337,0.01565047],"genre_scores_gemma":[0.8260761,0.01922775,0.1432277,0.001668166,0.003607317,0.000410592,0.001634103,0.0001488658,0.003999365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01178587,"threshold_uncertainty_score":0.06233037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1810314632060503,"score_gpt":0.3779403065980438,"score_spread":0.1969088433919935,"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."}}