{"id":"W4293863407","doi":"10.1109/siu55565.2022.9864669","title":"Active Learning for Online Nonlinear Neyman-Pearson Classification","year":2022,"lang":"en","type":"article","venue":"2022 30th Signal Processing and Communications Applications Conference (SIU)","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Stantec (Canada)","funders":"","keywords":"Binary classification; Artificial intelligence; Computer science; Machine learning; Random forest; Context (archaeology); Ensemble learning; Constant false alarm rate; Decision tree; False alarm; Pattern recognition (psychology); Set (abstract data type); Data mining; Support vector machine","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","sts"],"consensus_categories":[],"category_scores_codex":[0.000435925,0.0002210786,0.0002262701,0.0002492014,0.003964501,0.0003153372,0.002103732,0.00007695256,0.00004936555],"category_scores_gemma":[0.00001797664,0.0002546292,0.00008477906,0.001185649,0.0002155873,0.0004364526,0.0009733734,0.0007152921,0.000008095409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001328012,"about_ca_system_score_gemma":0.0003342137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003373601,"about_ca_topic_score_gemma":0.00001573827,"domain_scores_codex":[0.9981198,0.000176017,0.0004641044,0.000657521,0.0002855076,0.0002970388],"domain_scores_gemma":[0.9974436,0.0002226904,0.0004416734,0.00132266,0.0004454005,0.0001239846],"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":[0.00001220339,0.0003886671,0.00004880569,0.0000274535,0.00001640714,9.745449e-8,0.0005245395,0.0001520591,0.004238018,0.1233281,0.0002113052,0.8710523],"study_design_scores_gemma":[0.0002679701,0.0001474072,0.000392597,0.00001367789,0.00003031898,0.00001680514,0.001856687,0.6651941,0.000430328,0.01063872,0.3207035,0.0003079013],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001324838,0.000706759,0.9882638,0.006166351,0.00001536943,0.001164084,0.00009564517,0.0006016887,0.001661418],"genre_scores_gemma":[0.8252465,0.0004033167,0.1642,0.0003065001,0.00005387938,0.008311986,0.0005223345,0.00002850675,0.0009270206],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8707444,"threshold_uncertainty_score":0.9999906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05493545051714688,"score_gpt":0.3136604428057829,"score_spread":0.258724992288636,"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."}}