{"id":"W2079259110","doi":"10.1109/est.2012.31","title":"Optimum-Path Forest Classifier for Large Scale Biometric Applications","year":2012,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Government of Canada","keywords":"Computer science; Iris recognition; Biometrics; Classifier (UML); Artificial intelligence; Pattern recognition (psychology); Naive Bayes classifier; Hamming distance; Random forest; Machine learning; Data mining; Algorithm; Support vector machine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001025441,0.0003355929,0.0004549087,0.00105116,0.0004063091,0.0004075296,0.000590153,0.0007036616,0.002278084],"category_scores_gemma":[0.002842946,0.0001529186,0.0003083775,0.001107244,0.0002468624,0.0009880753,0.0003238558,0.0005215426,0.0008268314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003351814,"about_ca_system_score_gemma":0.0006860089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003473933,"about_ca_topic_score_gemma":0.004472807,"domain_scores_codex":[0.999592,0.0001009491,0.00002055003,0.00008276497,0.0001580196,0.00004577966],"domain_scores_gemma":[0.9991325,0.0004681611,0.00006637083,0.00009522011,0.0002079576,0.00002985925],"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":[0.0003404941,0.0001439466,0.00382003,0.0001115778,0.00003829083,0.0001600882,0.00004751593,0.1458403,0.0282975,0.006302704,0.004993077,0.8099045],"study_design_scores_gemma":[0.00001288994,0.00005434583,0.002081613,0.000006772229,0.000007525392,0.0001264744,0.00001744895,0.9858186,0.00465964,0.005032507,0.002171878,0.0000102288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0512019,0.0004697587,0.9456798,0.0001669376,0.00004234959,0.0000824591,0.0002199255,0.001145559,0.000991361],"genre_scores_gemma":[0.3861291,0.0003498975,0.610633,0.00005916549,0.00004850437,0.0001457005,0.0007489651,0.00006727668,0.001818402],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003473933,"threshold_uncertainty_score":0.00762099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02023585257663407,"score_gpt":0.2945484980927995,"score_spread":0.2743126455161655,"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."}}