{"id":"W4405041883","doi":"10.1016/j.ejor.2024.11.047","title":"An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Scientific Research Fund of Liaoning Provincial Education Department; Ministry of Education of the People's Republic of China; Humanities and Social Science Fund of Ministry of Education of China; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Preference elicitation; Sorting; Preference; Monotonic function; Computer science; Preference learning; Artificial intelligence; Machine learning; Operations research; Mathematics; Statistics; Algorithm","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.007090473,0.0001224611,0.0001484715,0.0006576733,0.0002849702,0.001570123,0.001169748,0.00002266108,0.00003017478],"category_scores_gemma":[0.0003893371,0.00009723246,0.00005165144,0.0007590276,0.00004995458,0.001033417,0.0001808101,0.0007558696,0.00006873666],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001083802,"about_ca_system_score_gemma":0.0005584677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002517185,"about_ca_topic_score_gemma":0.000007074812,"domain_scores_codex":[0.9960845,0.001574819,0.0005901264,0.0003699879,0.001041859,0.0003387802],"domain_scores_gemma":[0.998816,0.0002125624,0.00007291386,0.0001807112,0.000524994,0.0001928342],"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":[0.000235648,0.001462269,0.004510253,0.0001676644,0.00008563157,0.0008350287,0.01669783,0.5747218,0.1178663,0.01060708,0.0009820267,0.2718285],"study_design_scores_gemma":[0.0004646187,0.001017697,0.03480445,0.0002108691,0.000002195901,0.00002800794,0.0003887161,0.9617463,0.0005049755,0.0001174263,0.0005581264,0.0001566402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2989904,0.0001198049,0.6902833,0.0005791821,0.0001760625,0.0002503779,0.00000204296,0.00002632142,0.009572496],"genre_scores_gemma":[0.8343698,0.0000146904,0.1653251,0.00007901063,0.0001334586,0.000006650852,0.000007643156,0.00001347616,0.00005018575],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5353794,"threshold_uncertainty_score":0.9994664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1757159714961851,"score_gpt":0.3943611793032988,"score_spread":0.2186452078071137,"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."}}