{"id":"W2481773658","doi":"10.1016/j.compmedimag.2016.07.011","title":"Ensemble based adaptive over-sampling method for imbalanced data learning in computer aided detection of microaneurysm","year":2016,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":78,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Ensemble learning; Adaptive sampling; Sampling (signal processing); Machine learning; Pattern recognition (psychology); Training set; Computer vision; Mathematics; Monte Carlo method; Statistics","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":[0.001651438,0.0001960795,0.0003830316,0.0003309848,0.0001020549,0.00006985525,0.001103638,0.0001122498,0.000001364621],"category_scores_gemma":[0.0004191616,0.0001583933,0.00005829513,0.0004323779,0.0001748742,0.0004869759,0.0006238259,0.000260615,4.152498e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003083218,"about_ca_system_score_gemma":0.0001181182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003219222,"about_ca_topic_score_gemma":0.000007863538,"domain_scores_codex":[0.9977788,0.0002594124,0.0005077849,0.0007739202,0.0003458075,0.0003342504],"domain_scores_gemma":[0.9970336,0.001604313,0.000259529,0.0008165236,0.0001422703,0.0001437669],"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":[0.00006408686,0.00007111306,0.001086102,0.00007298789,0.00001536947,0.000008148858,0.00006677475,0.00001589183,0.1393361,0.003080877,0.0001480735,0.8560345],"study_design_scores_gemma":[0.002006874,0.00009103019,0.003484388,0.0003221096,0.000007784509,0.00002500983,0.000003554711,0.9745825,0.01469704,0.002873424,0.001697533,0.0002087545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002722848,0.00009302872,0.9947021,0.001609693,0.0002705203,0.0002865194,0.00002501467,0.0002867025,0.000003589224],"genre_scores_gemma":[0.334913,0.00007184496,0.6643053,0.0005739781,0.00006626686,0.00002483327,0.00002809088,0.00001433948,0.000002406077],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9745666,"threshold_uncertainty_score":0.6459092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03898227865516279,"score_gpt":0.3250309721664764,"score_spread":0.2860486935113136,"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."}}