{"id":"W4396523557","doi":"10.1109/tfuzz.2024.3394897","title":"A Linguistically Interpretable Deep Fuzzy Classification System With Feature Transformation and Reconstruction","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Interpretability; Computer science; Artificial intelligence; Fuzzy logic; Classifier (UML); Fuzzy rule; Machine learning; Feature (linguistics); Feature vector; Fast Fourier transform; Fuzzy control system; Pattern recognition (psychology); Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007200378,0.0005910362,0.0006722221,0.0005025235,0.0004579118,0.0008944542,0.001257796,0.001101083,0.002227711],"category_scores_gemma":[0.001636728,0.0002528266,0.0006368685,0.0003907823,0.0004034852,0.001014737,0.0007389403,0.001181614,0.001009915],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006284203,"about_ca_system_score_gemma":0.001056477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005099838,"about_ca_topic_score_gemma":0.006935127,"domain_scores_codex":[0.999685,0.00003861608,0.0000266401,0.0001015109,0.0001059892,0.00004224926],"domain_scores_gemma":[0.9994697,0.0001231935,0.00005492604,0.00009853095,0.0002244842,0.00002919065],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003899328,0.000194316,0.001855975,0.0001345145,0.0001022008,0.0002463827,0.0001686958,0.137624,0.08092168,0.007705141,0.005305769,0.7653515],"study_design_scores_gemma":[0.00001238497,0.00007559335,0.0003921324,0.00001499049,0.00002208359,0.0001007091,0.00002079811,0.981325,0.01336475,0.003187263,0.001463218,0.00002092128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03024589,0.0001768103,0.9648961,0.0002233146,0.00007103496,0.00006526546,0.0001542646,0.002667552,0.001499763],"genre_scores_gemma":[0.445353,0.000144948,0.5493502,0.0003596724,0.0000583026,0.0001288523,0.0004955397,0.00008141554,0.004028122],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005099838,"threshold_uncertainty_score":0.0101403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01008462943273214,"score_gpt":0.2105228486489982,"score_spread":0.200438219216266,"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."}}