{"id":"W4402263393","doi":"10.1109/tits.2024.3445664","title":"A New Decision Tree Based on Intuitionistic Fuzzy Twin Support Vector Machines","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"National Key Research and Development Program of China; Yunnan Key Research and Development Program; University of Electronic Science and Technology of China","keywords":"Decision tree; Computer science; Support vector machine; Artificial intelligence; Data mining; Machine learning","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001876943,0.0003186335,0.0002543392,0.0005291408,0.0002521709,0.0003608296,0.0004869069,0.0001208521,0.0001307825],"category_scores_gemma":[0.000003188043,0.0002984498,0.0002571501,0.001034976,0.0000314444,0.0003795022,5.953744e-7,0.000312354,0.0004399135],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001856669,"about_ca_system_score_gemma":0.000236098,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000084027,"about_ca_topic_score_gemma":0.00005968993,"domain_scores_codex":[0.9975535,0.000049111,0.0007223063,0.0007351573,0.000675264,0.000264658],"domain_scores_gemma":[0.9985369,0.0004876706,0.00009363684,0.0005327297,0.000138618,0.000210399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000517095,0.0001905636,0.000003359537,0.00008349881,0.00003904527,0.00003150843,0.0003349679,0.6684449,0.0002840551,0.1113121,0.001306226,0.2179181],"study_design_scores_gemma":[0.0003751036,0.0006557789,0.0001925224,0.0006524309,0.00006433099,0.00002044973,0.00003706484,0.9478508,0.008529129,0.01615123,0.02486872,0.0006024354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002389381,0.00006344695,0.9944827,0.0006689536,0.001809666,0.0006999046,0.0001430858,0.00117033,0.0007229906],"genre_scores_gemma":[0.9468844,0.0000356656,0.05128661,0.0002557989,0.0001180475,0.0004053832,0.00006979363,0.00004299134,0.0009013372],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9466454,"threshold_uncertainty_score":0.9999468,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0221876990650716,"score_gpt":0.2952417327130866,"score_spread":0.2730540336480149,"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."}}