{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003191387,0.0001225242,0.0001159628,0.00008472979,0.0002575897,0.000249188,0.0006214365,0.00002770864,2.377048e-7],"category_scores_gemma":[0.00002270419,0.00008976582,0.00001245904,0.0004118881,0.0001491573,0.000477184,0.00006135413,0.0001562984,0.000006052335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001194024,"about_ca_system_score_gemma":0.00002898669,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00014069,"about_ca_topic_score_gemma":0.000008733045,"domain_scores_codex":[0.9986579,0.00004315586,0.0001204631,0.0006105036,0.0002752313,0.0002927207],"domain_scores_gemma":[0.998949,0.00003251263,0.00008252166,0.0008195505,0.00002893468,0.00008751877],"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":[0.000003703493,0.00000695722,0.00003871868,0.000003146161,0.000002297799,0.00003843048,0.0002367268,0.00007512911,0.07544196,0.00005891253,0.0006972666,0.9233968],"study_design_scores_gemma":[0.0001999313,0.00007800688,0.01216234,0.0000486965,0.000004381924,0.0001912963,0.0000156924,0.9847521,0.00049559,0.0001204658,0.001745356,0.0001860945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2036134,0.00002182989,0.7693011,0.02645465,0.0002398912,0.0000527281,7.029449e-7,0.0001191852,0.0001964989],"genre_scores_gemma":[0.5012035,0.0000184607,0.4911604,0.007246634,0.0001628069,1.435682e-8,0.000005513935,0.000004796436,0.0001978785],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.984677,"threshold_uncertainty_score":0.3660544,"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."}}