{"id":"W3164230923","doi":"10.1145/3441250.3441274","title":"Threshold Moving Approaches for Addressing the Class Imbalance Problem and their Application to Multi-label Classification","year":2020,"lang":"en","type":"article","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multi-label classification; Class (philosophy); Computer science; Relevance (law); Artificial intelligence; Binary classification; Training set; Binary number; Machine learning; Simple (philosophy); Pattern recognition (psychology); Data mining; Mathematics; Support vector machine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.006329698,0.001118388,0.001500948,0.00335229,0.001372911,0.001891897,0.002826554,0.002078978,0.001976367],"category_scores_gemma":[0.01292529,0.0004755211,0.001207964,0.003528408,0.001666858,0.003458087,0.001974237,0.003536699,0.00121201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009047786,"about_ca_system_score_gemma":0.001088941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001261374,"about_ca_topic_score_gemma":0.001212555,"domain_scores_codex":[0.995531,0.001229636,0.0002338187,0.000673274,0.002116919,0.0002153236],"domain_scores_gemma":[0.9936869,0.003237557,0.0007729594,0.0008530798,0.001225497,0.0002240452],"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":[0.0003068354,0.0002429058,0.001766048,0.0004379424,0.0001518905,0.000214544,0.0006108296,0.04038803,0.02640562,0.02831634,0.005241624,0.8959174],"study_design_scores_gemma":[0.0001012405,0.0004540243,0.003646377,0.0002124396,0.0001797415,0.0009303597,0.0004099841,0.8273107,0.0305099,0.1119462,0.02408384,0.0002153236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005504262,0.001322236,0.9915651,0.0002058283,0.0001684204,0.0001028791,0.00002435307,0.000443206,0.0006637685],"genre_scores_gemma":[0.1901204,0.001757263,0.8042771,0.0003831751,0.0006482995,0.0003270857,0.0001978184,0.0002236475,0.002065257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006329698,"threshold_uncertainty_score":0.03347504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2065772010912053,"score_gpt":0.3141587319339968,"score_spread":0.1075815308427915,"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."}}