{"id":"W3213957636","doi":"","title":"Fuzzy kernel K-medoids algorithm for multiclass multidimensional data classification","year":2015,"lang":"en","type":"article","venue":"Journal of Theoretical and Applied Information Technology","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Multiclass classification; Artificial intelligence; Pattern recognition (psychology); Medoid; k-medoids; Kernel (algebra); Data mining; Fuzzy logic; Machine learning; Algorithm; Support vector machine; Mathematics; Fuzzy clustering; Cluster analysis; Discrete 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006775059,0.00008744314,0.0001615733,0.0002436543,0.0000683954,0.00006198907,0.00056239,0.0001609347,0.000001425259],"category_scores_gemma":[0.0002249137,0.00006352287,0.00002463979,0.0001776819,0.0002144979,0.001025975,0.0002942623,0.0001810171,0.00002092164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000210916,"about_ca_system_score_gemma":0.00006270368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":2.091112e-7,"about_ca_topic_score_gemma":3.722549e-8,"domain_scores_codex":[0.9990439,0.00001449324,0.0004614261,0.000108483,0.0002298668,0.0001418413],"domain_scores_gemma":[0.9988737,0.0001009721,0.0003039677,0.000283278,0.0003261515,0.0001119807],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002294588,0.00002582818,0.00000253283,0.000004813039,0.000007447081,2.467937e-7,0.00007550572,0.000008594267,0.000420462,0.5946512,0.002294629,0.4024858],"study_design_scores_gemma":[0.002162474,0.0002290778,0.0000321462,0.00002680506,0.0000174348,0.0001198414,0.000939052,0.4750233,0.01046888,0.4896284,0.02120615,0.0001464228],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004624005,0.00002990671,0.9885976,0.005264943,0.0002045425,0.0001666886,0.00001556924,0.00005609816,0.001040687],"genre_scores_gemma":[0.4021102,0.00002070055,0.597352,0.0004088412,0.00005237124,0.00001298172,0.00003319684,0.000003382593,0.000006287323],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4750147,"threshold_uncertainty_score":0.2590388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02409456770105158,"score_gpt":0.2672727758607576,"score_spread":0.2431782081597061,"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."}}