{"id":"W2537424881","doi":"10.1109/embc.2016.7591262","title":"An empirical study on the effect of imbalanced data on bleeding detection in endoscopic video","year":2016,"lang":"en","type":"article","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Classifier (UML); Artificial intelligence; Computer science; Training set; Pattern recognition (psychology); Machine learning; Class (philosophy)","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":[0.001268723,0.0001175114,0.0001597088,0.0001455286,0.00005079957,0.00004850319,0.001949453,0.00004221677,0.000009311302],"category_scores_gemma":[0.0003387002,0.00005582389,0.000015156,0.0003794961,0.00003578415,0.000569084,0.0002717616,0.000112507,0.0000268247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006700799,"about_ca_system_score_gemma":0.0000154462,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000025998,"about_ca_topic_score_gemma":0.00003318589,"domain_scores_codex":[0.9983833,0.0003907671,0.0002424784,0.0005202621,0.0002969886,0.0001661885],"domain_scores_gemma":[0.9965943,0.0008969746,0.00009880454,0.002353999,0.00002376822,0.00003216929],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001575761,0.0006913419,0.2514577,0.00001463509,0.00002552281,0.000006283773,0.0004261971,0.000004433698,0.4857759,0.004588797,0.001347883,0.2555038],"study_design_scores_gemma":[0.000746179,0.003241531,0.2157903,0.00006019987,0.000003290149,7.95396e-7,0.00003139118,0.006381996,0.7732747,0.00025101,0.0001169832,0.0001016204],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6931363,0.000001398536,0.3046518,0.000801623,0.0001064879,0.0005910696,0.000006827742,0.0002334281,0.0004710644],"genre_scores_gemma":[0.9985803,0.000002127817,0.001177595,0.0001196005,0.00002378126,0.00006953525,0.000002188102,0.000006404259,0.00001851449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3054439,"threshold_uncertainty_score":0.3622602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04813193729786212,"score_gpt":0.3521939576859169,"score_spread":0.3040620203880548,"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."}}