{"id":"W4387068273","doi":"10.1109/tai.2023.3319301","title":"Double-Quantitative Feature Selection Approach for Multigranularity Ordered Decision Systems","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Chongqing; China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Viewpoints; Granular computing; Feature selection; Data mining; Feature (linguistics); Completeness (order theory); Focus (optics); Perspective (graphical); Artificial intelligence; Selection (genetic algorithm); Greedy algorithm; Rough set; Machine learning; Algorithm; 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":[0.002513043,0.0009932242,0.001436956,0.002442969,0.0006056296,0.001768486,0.0008784353,0.0005802786,0.00112691],"category_scores_gemma":[0.004306387,0.0003256273,0.001263464,0.001713954,0.0005666955,0.001422593,0.001014634,0.0007837356,0.0001509639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001120532,"about_ca_system_score_gemma":0.001219941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00175546,"about_ca_topic_score_gemma":0.001332764,"domain_scores_codex":[0.9974313,0.000688988,0.0002671622,0.0004054761,0.001018173,0.0001888623],"domain_scores_gemma":[0.9982066,0.0009962401,0.0001929884,0.0001181082,0.0004137427,0.00007221745],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003763923,0.0001991153,0.00546204,0.0006760829,0.0003244407,0.000452835,0.0004028637,0.5249363,0.01716063,0.03704929,0.0025885,0.4103715],"study_design_scores_gemma":[0.00002632656,0.0001020281,0.000988552,0.00002816452,0.00005291627,0.00009579989,0.00005866745,0.976567,0.002904664,0.0175085,0.00164104,0.00002642287],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01900761,0.0003831758,0.97938,0.0001457536,0.00003157678,0.00008408679,0.00008157786,0.0002205091,0.00066573],"genre_scores_gemma":[0.6274413,0.0002729926,0.3709863,0.0001017678,0.00006221987,0.0002638428,0.0002741664,0.00003600193,0.0005614014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002513043,"threshold_uncertainty_score":0.01329035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1139263091861794,"score_gpt":0.339383797119346,"score_spread":0.2254574879331666,"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."}}