{"id":"W4386322208","doi":"10.1109/tpami.2023.3310908","title":"Handling Multi-Class Problem by Intuitionistic Fuzzy Twin Support Vector Machines Based on Relative Density Information","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Support vector machine; Class (philosophy); Computer science; Pattern recognition (psychology); Machine learning; Mathematics; Data mining","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":[0.0003298255,0.0002425588,0.0002770205,0.0007620104,0.0004305322,0.0002505402,0.0002618147,0.00009552983,0.0001158825],"category_scores_gemma":[0.000015793,0.000209387,0.0001967768,0.001428787,0.00005212742,0.0007161619,0.000007699894,0.0003240618,0.0003489327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004984425,"about_ca_system_score_gemma":0.00002950496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005542226,"about_ca_topic_score_gemma":0.0003836124,"domain_scores_codex":[0.998431,0.0001188443,0.0004417645,0.0004243854,0.0003281742,0.0002557865],"domain_scores_gemma":[0.9990116,0.0002314522,0.0001561189,0.0003394247,0.0001274558,0.000133913],"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":[0.00005999358,0.0003859477,0.0007992682,0.00007689492,0.0003580514,0.00001215603,0.001206355,0.1682262,0.001055122,0.0001022366,0.0005541543,0.8271636],"study_design_scores_gemma":[0.0002173881,0.0001833619,0.0006198931,0.00006950641,0.0001681524,0.00000255589,0.0000391653,0.9543889,0.04358348,0.0003211007,0.0001421751,0.0002643759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00389109,0.000006707593,0.9943308,0.0008435458,0.000184709,0.0002048832,0.0001991966,0.0002322391,0.0001068018],"genre_scores_gemma":[0.9967899,0.00008736861,0.001776218,0.0008619107,0.000009754533,0.00005941259,0.0002001671,0.00000931141,0.0002059116],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9928989,"threshold_uncertainty_score":0.8538554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01965846418562398,"score_gpt":0.2621525064466393,"score_spread":0.2424940422610154,"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."}}