{"id":"W2119480209","doi":"10.1109/lgrs.2009.2021964","title":"Mine Classification With Imbalanced Data","year":2009,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"ca_institutions":"Defence Research and Development Canada","funders":"","keywords":"Clutter; Computer science; Sonar; One-class classification; Data mining; Statistical classification; Class (philosophy); Data set; Radar; Logistic regression; Support vector machine; Set (abstract data type); Classification rule; Artificial intelligence; Remote sensing; Pattern recognition (psychology); Machine learning; Geology","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":[0.0079179,0.0009870275,0.001596578,0.00252016,0.000884413,0.002047805,0.001654448,0.001638232,0.0006915629],"category_scores_gemma":[0.0213571,0.0004579384,0.0008244893,0.002480191,0.001076514,0.003223121,0.002267132,0.001677631,0.0006770493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008853931,"about_ca_system_score_gemma":0.000590789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001226866,"about_ca_topic_score_gemma":0.0009150264,"domain_scores_codex":[0.9957093,0.001479374,0.0003339016,0.0009171905,0.001234029,0.0003262364],"domain_scores_gemma":[0.9889216,0.006105804,0.001305295,0.001758996,0.00166343,0.0002448272],"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":[0.001985074,0.0008399546,0.0694162,0.0003258487,0.0002985264,0.0009752067,0.0006497305,0.3507281,0.01165956,0.007411036,0.008994017,0.5467168],"study_design_scores_gemma":[0.00004293911,0.0001270584,0.007580229,0.00002107473,0.00002696022,0.0002339927,0.0002011895,0.972349,0.004599001,0.01294946,0.001838345,0.00003067766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3168711,0.0009914254,0.6751972,0.001412349,0.0003062606,0.0001959315,0.0009159952,0.00138822,0.002721501],"genre_scores_gemma":[0.8864639,0.0003077005,0.1094817,0.0003150777,0.0003375196,0.0001668978,0.001792091,0.00006886125,0.001066311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0079179,"threshold_uncertainty_score":0.04187435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02251171805911829,"score_gpt":0.2586820343499466,"score_spread":0.2361703162908284,"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."}}