{"id":"W3130326228","doi":"10.1109/icdmw51313.2020.00045","title":"Temporally-Reweighted Dirichlet Process Mixture Anomaly Detector","year":2020,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Anomaly detection; Dirichlet process; Computer science; Streaming data; Naive Bayes classifier; Detector; Parametric statistics; Artificial intelligence; Process (computing); Data mining; Bayesian probability; Anomaly (physics); Algorithm; Pattern recognition (psychology); Mathematics; Support vector machine; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002765107,0.0008765253,0.001922388,0.001810582,0.0006426264,0.001242185,0.003323202,0.001629913,0.001387278],"category_scores_gemma":[0.007586563,0.0007311287,0.001276205,0.001661136,0.0008525415,0.002362663,0.001887542,0.002578699,0.0007924949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001088345,"about_ca_system_score_gemma":0.001208204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004419784,"about_ca_topic_score_gemma":0.004174857,"domain_scores_codex":[0.9979797,0.0005632642,0.0001052617,0.0006327686,0.0005674033,0.000151603],"domain_scores_gemma":[0.9976202,0.001322145,0.0001715212,0.0002776905,0.0005147249,0.00009377368],"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":[0.0003167346,0.0001776405,0.005548482,0.0001221562,0.0002692792,0.0002134227,0.0002728088,0.4406824,0.01278816,0.04569492,0.005628322,0.4882856],"study_design_scores_gemma":[0.000004761947,0.000009103936,0.0001767599,0.000003020557,0.000006482705,0.0000478986,0.000006868081,0.9890949,0.001301745,0.008640978,0.0006975885,0.000009925744],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004918022,0.0001030596,0.9942077,0.00007513702,0.00002840699,0.00001670805,0.00004428863,0.0003466278,0.0002599425],"genre_scores_gemma":[0.2810189,0.0002489739,0.7143333,0.000185012,0.0001349065,0.0001272003,0.0006253476,0.0002829979,0.003043417],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004419784,"threshold_uncertainty_score":0.01462346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0189801902305601,"score_gpt":0.2568452401752701,"score_spread":0.23786504994471,"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."}}