{"id":"W4390357743","doi":"10.1109/tfuzz.2023.3347757","title":"Feature Selection Using Zentropy-Based Uncertainty Measure","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Natural Science Foundation of China","keywords":"Feature selection; Entropy (arrow of time); Computer science; Artificial intelligence; Data mining; Measure (data warehouse); Feature (linguistics); Machine learning; Stability (learning theory); Granular computing; Information theory; Feature vector; Pattern recognition (psychology); Rough set; Mathematics; Statistics","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":[0.0004139043,0.0002514472,0.0002680892,0.0003919528,0.0005608787,0.0003128017,0.0004763976,0.0001993322,0.000004641384],"category_scores_gemma":[0.00000487411,0.0002174884,0.0001821917,0.001960998,0.0000326773,0.0002872389,0.000002083934,0.0003522918,0.0002265605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002403282,"about_ca_system_score_gemma":0.0001598491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002699432,"about_ca_topic_score_gemma":0.00003861459,"domain_scores_codex":[0.9979376,0.0002202858,0.0002333375,0.0005447224,0.0005863687,0.0004776926],"domain_scores_gemma":[0.998978,0.00009684449,0.000103996,0.0005245865,0.0001491634,0.0001474652],"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":[0.00002322016,0.00007447291,0.00002298084,0.00005209064,0.00004778229,0.00002008361,0.0001421321,0.9869292,0.003651445,0.0005593243,0.00236604,0.00611126],"study_design_scores_gemma":[0.0005263591,0.0001484128,0.00006595899,0.00008952535,0.00002706149,0.00004181032,0.00007694797,0.9935673,0.001522131,0.00009486119,0.003533607,0.000305988],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00472398,0.00006741072,0.9882597,0.0009822015,0.003716523,0.000452642,0.00002557972,0.001112665,0.0006593324],"genre_scores_gemma":[0.9960982,0.000008595914,0.002986126,0.0001422415,0.0001696507,0.00006065923,0.000005440876,0.00002685322,0.0005021945],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9913743,"threshold_uncertainty_score":0.8868921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03645288838981205,"score_gpt":0.2554404471021534,"score_spread":0.2189875587123413,"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."}}