{"id":"W2743178907","doi":"10.1111/coin.12128","title":"Learning over subconcepts: Strategies for 1‐class classification","year":2017,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Health Canada","keywords":"Computer science; Property (philosophy); Class (philosophy); Machine learning; Artificial intelligence; Domain (mathematical analysis); Multiclass classification; One-class classification; Classifier (UML); Support vector machine; Mathematics","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":[0.008755038,0.0007369453,0.001130709,0.002668578,0.001302678,0.002382319,0.002950739,0.001266446,0.002647312],"category_scores_gemma":[0.02535384,0.0004075044,0.0009386306,0.001946947,0.002869107,0.005888539,0.004001867,0.002758815,0.0005737107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001729183,"about_ca_system_score_gemma":0.001255926,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001598014,"about_ca_topic_score_gemma":0.001694149,"domain_scores_codex":[0.9954273,0.002158856,0.0002890115,0.0009892875,0.0009282087,0.0002073683],"domain_scores_gemma":[0.9802138,0.01341461,0.001263679,0.002693938,0.001786824,0.000627176],"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":[0.0002552489,0.0003686813,0.006917029,0.0002092892,0.0001020452,0.0001547837,0.001857239,0.06442801,0.002827498,0.285754,0.004413621,0.6327125],"study_design_scores_gemma":[0.00002705682,0.00005289962,0.0006516201,0.00006512929,0.00003229898,0.00008681359,0.0002605013,0.6425344,0.002431857,0.3494546,0.00437592,0.00002688021],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03465805,0.0004281765,0.9600027,0.001035961,0.0000375961,0.0001547532,0.00005333275,0.0002260476,0.003403391],"genre_scores_gemma":[0.4288119,0.0002406497,0.5674117,0.0005161631,0.0001145021,0.0004294117,0.0002341972,0.00008916709,0.002152353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008755038,"threshold_uncertainty_score":0.04630166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0847742673357272,"score_gpt":0.374068715977271,"score_spread":0.2892944486415438,"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."}}