{"id":"W2144252306","doi":"10.6000/1929-6029.2013.02.02.06","title":"A Comparison of Error Correcting Output Coding Methods for Multiclass Classification by Using Support Vector Machine: The Prediction of Self-Monitoring of Blood Sugar","year":2013,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Multiclass classification; Support vector machine; Artificial intelligence; Binary number; Computer science; Machine learning; Structured support vector machine; Binary classification; Pattern recognition (psychology); Coding (social sciences); Class (philosophy); AdaBoost; Data mining; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00452832,0.0007083019,0.0009038639,0.001881025,0.0003980628,0.001017413,0.0009877064,0.00104999,0.0006049137],"category_scores_gemma":[0.01225817,0.0002424706,0.0006347616,0.001313474,0.0004759564,0.001230528,0.0006764508,0.001103466,0.0002332379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007447291,"about_ca_system_score_gemma":0.0009888017,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005729394,"about_ca_topic_score_gemma":0.002625535,"domain_scores_codex":[0.9974323,0.0008493978,0.0001631984,0.0003439627,0.001066883,0.0001441946],"domain_scores_gemma":[0.9898705,0.006013033,0.0004778378,0.0007340377,0.002734701,0.0001699348],"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":[0.002039877,0.0004433885,0.01037559,0.0003072367,0.000286302,0.00009861485,0.0002877742,0.1270973,0.0087385,0.003351843,0.002010639,0.844963],"study_design_scores_gemma":[0.00003659069,0.0003640762,0.004994906,0.00004232214,0.00006451374,0.000077907,0.00007998821,0.9845047,0.008034239,0.0007943886,0.0009631868,0.0000431418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.368024,0.006548603,0.6182645,0.0006311742,0.0004927214,0.000184958,0.0002064635,0.00174064,0.003906923],"genre_scores_gemma":[0.812745,0.001382305,0.183623,0.00009566324,0.00008939137,0.0001310848,0.000355415,0.00009275095,0.001485419],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005729394,"threshold_uncertainty_score":0.02394831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2169938359299989,"score_gpt":0.5356901599460219,"score_spread":0.3186963240160229,"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."}}