{"id":"W1837658860","doi":"10.1007/978-3-540-69812-8_89","title":"Optimal Features Subset Selection Using Genetic Algorithms for Iris Recognition","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Iris recognition; Computer science; Biometrics; Feature selection; Pattern recognition (psychology); Artificial intelligence; Selection (genetic algorithm); IRIS (biosensor); Principal component analysis; Feature (linguistics); Gaussian","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005626241,0.0004051728,0.0003803804,0.001696375,0.0005172989,0.000589816,0.001602432,0.0004255214,0.00001424586],"category_scores_gemma":[0.0001043192,0.0004109324,0.000168297,0.0015332,0.0003798044,0.0005139096,0.000342089,0.0005272654,0.00001969439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003994267,"about_ca_system_score_gemma":0.0005175268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005089497,"about_ca_topic_score_gemma":0.00002483634,"domain_scores_codex":[0.9967037,0.00003754345,0.00047921,0.001435506,0.0007949923,0.0005490751],"domain_scores_gemma":[0.9980317,0.0002593635,0.0003277075,0.0006502441,0.0005846116,0.0001463752],"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":[0.000008462464,0.0000430751,0.00001698366,0.00004051051,0.00001508979,0.00001870545,0.0005832276,0.01679862,0.0002908306,0.0002500369,0.0003853843,0.9815491],"study_design_scores_gemma":[0.0003283078,0.0001627058,0.000452687,0.0001050341,0.00001557116,0.0004272016,1.58744e-7,0.9788526,0.002897686,0.01099836,0.005078103,0.0006816413],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004122863,0.0006699141,0.9958903,0.0002282414,0.001921408,0.0005899223,0.00002833605,0.0001499195,0.0001096244],"genre_scores_gemma":[0.00767425,0.0001506302,0.9907163,0.0005666442,0.0005968509,0.00001563736,0.00003534526,0.00002901487,0.0002152673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9808674,"threshold_uncertainty_score":0.9998342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05175913288844276,"score_gpt":0.2805869966771369,"score_spread":0.2288278637886941,"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."}}