{"id":"W561402864","doi":"10.1016/j.neucom.2015.05.102","title":"Granular fuzzy modeling with evolving hyperboxes in multi-dimensional space of numerical data","year":2015,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Construct (python library); Computer science; Cluster analysis; Algorithm; Granular computing; Partition (number theory); Basis (linear algebra); Parametric statistics; Artificial neural network; Set (abstract data type); Context (archaeology); Fuzzy logic; Data mining; Mathematics; Artificial intelligence; Rough set","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003598476,0.000125653,0.0001921231,0.0001158645,0.00005869462,0.00005251326,0.0007106542,0.00004047046,0.000001038889],"category_scores_gemma":[0.0001102816,0.0001057155,0.00002050002,0.0003671917,0.000018785,0.0005539819,0.0007632123,0.0001944095,0.00001078702],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001461805,"about_ca_system_score_gemma":0.00008290865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001536523,"about_ca_topic_score_gemma":0.000007811027,"domain_scores_codex":[0.9985508,0.00009279124,0.0002564545,0.0004846524,0.0003672382,0.0002480679],"domain_scores_gemma":[0.9990596,0.00009651054,0.00009516902,0.0005247589,0.0001212841,0.0001026706],"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":[0.00003373818,0.0003086759,0.01277002,0.00004889659,0.000007552952,0.0001055297,0.001207025,0.9606001,0.00778041,0.000331499,0.000489982,0.01631655],"study_design_scores_gemma":[0.000646975,0.00005358851,0.000627358,0.0001457332,0.000002927672,0.00002948105,0.00005290677,0.9975287,0.0005936823,0.0001610581,0.00003340328,0.0001242395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3500961,0.00008684077,0.6491966,0.0002036125,0.0001102132,0.00009519197,0.000001223967,0.0000708712,0.000139388],"genre_scores_gemma":[0.7636202,9.39606e-7,0.2362396,0.00009542653,0.00002491033,0.000001339896,0.000005573796,0.000008769981,0.000003251655],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4135241,"threshold_uncertainty_score":0.4310952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100664574853255,"score_gpt":0.2894161789787335,"score_spread":0.1887516041254785,"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."}}