{"id":"W2952935381","doi":"10.1016/j.ins.2019.06.003","title":"Granularity-driven sequential three-way decisions: A cost-sensitive approach to classification","year":2019,"lang":"en","type":"article","venue":"Information Sciences","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"Sichuan Province Youth Science and Technology Innovation Team; Special Fund for Distinguished Experts in Guangxi of China; Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Granularity; Computer science; Decision engineering; Process (computing); Key (lock); Decision model; Optimal decision; Decision analysis; Decision support system; Feature (linguistics); Business decision mapping; Artificial intelligence; Machine learning; Data mining; Decision tree; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007580555,0.001006354,0.002906923,0.002887238,0.0009803805,0.003955146,0.003355538,0.002062485,0.002922203],"category_scores_gemma":[0.02212303,0.0008997567,0.001868383,0.003498076,0.001937151,0.005447786,0.002615726,0.002624817,0.0002745371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002809945,"about_ca_system_score_gemma":0.002055086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004608929,"about_ca_topic_score_gemma":0.003230724,"domain_scores_codex":[0.9940525,0.002173604,0.0003461734,0.0009171797,0.001958425,0.0005521941],"domain_scores_gemma":[0.9837899,0.01192363,0.001227052,0.001273941,0.001370202,0.0004153525],"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":[0.0003851828,0.0001808445,0.003064361,0.0002292662,0.0002342293,0.0001877644,0.0003128195,0.7474006,0.002247465,0.1209875,0.001812311,0.1229576],"study_design_scores_gemma":[0.000006586185,0.00003060669,0.0003481667,0.00001137463,0.00002169828,0.00002093952,0.00002376354,0.9494013,0.0003492946,0.04949685,0.0002727162,0.0000166636],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02698686,0.0003233012,0.9704258,0.0004426086,0.00005173027,0.00009388838,0.0001130861,0.0001274895,0.001435194],"genre_scores_gemma":[0.6861991,0.0004155358,0.3105626,0.0001554469,0.0001555561,0.0001852656,0.0002379502,0.00008212774,0.00200637],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007580555,"threshold_uncertainty_score":0.04009026,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08468295428951868,"score_gpt":0.2977484559695738,"score_spread":0.2130655016800552,"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."}}