{"id":"W2895206473","doi":"10.1007/978-3-030-01851-1_28","title":"SCUT-DS: Learning from Multi-class Imbalanced Canadian Weather Data","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Oversampling; Computer science; Class (philosophy); Data stream; Machine learning; Binary number; Data stream mining; Artificial intelligence; Binary classification; Data mining; Support vector machine; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002103778,0.002420601,0.001297386,0.002771599,0.001345233,0.001945492,0.003934021,0.001209574,0.00529772],"category_scores_gemma":[0.00671978,0.0007099342,0.001321457,0.003497014,0.0006855949,0.002206851,0.002273005,0.003146347,0.003448927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003017268,"about_ca_system_score_gemma":0.006792926,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2310178,"about_ca_topic_score_gemma":0.3404038,"domain_scores_codex":[0.9989434,0.0001237015,0.00004450056,0.0002587945,0.0004649726,0.0001645622],"domain_scores_gemma":[0.9985134,0.0004004988,0.00005142112,0.0003690025,0.0005398158,0.0001258479],"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":[0.0002986833,0.0001479367,0.007116377,0.0002730882,0.00028405,0.00017795,0.0002036967,0.0738828,0.003208813,0.005319186,0.2075895,0.7014978],"study_design_scores_gemma":[0.00008450168,0.00008510907,0.004148773,0.00007006351,0.00009427509,0.0001356945,0.0002677985,0.9050195,0.007199369,0.01837136,0.06446066,0.00006286991],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0798803,0.007398959,0.7729045,0.002707673,0.00232576,0.0007554376,0.04601715,0.07268209,0.01532809],"genre_scores_gemma":[0.2886596,0.002937132,0.5490856,0.001182461,0.0006348132,0.0006369415,0.1217236,0.003451627,0.03168835],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7689822,"threshold_uncertainty_score":0.4593462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04505112479481433,"score_gpt":0.2753153436189509,"score_spread":0.2302642188241366,"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."}}