{"id":"W2101549186","doi":"10.1145/2594473.2594476","title":"Ensembles for unsupervised outlier detection","year":2014,"lang":"en","type":"article","venue":"ACM SIGKDD Explorations Newsletter","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":273,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Anomaly detection; Outlier; Cluster analysis; Artificial intelligence; Focus (optics); Machine learning; Unsupervised learning; Core (optical fiber); Ensemble learning; Data mining; Pattern recognition (psychology); Data science","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":[0.0002037994,0.0001521059,0.0001334638,0.0001559724,0.0004438764,0.0002128534,0.0007566311,0.00009119418,0.00001824386],"category_scores_gemma":[0.0001409847,0.0001477945,0.0001117287,0.000391161,0.00003353989,0.0007242203,0.0001409663,0.0001021698,0.0001875552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003474229,"about_ca_system_score_gemma":0.0000180736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001195514,"about_ca_topic_score_gemma":0.0000237652,"domain_scores_codex":[0.9988793,0.00005357308,0.0002717203,0.0004144664,0.0001409117,0.000240003],"domain_scores_gemma":[0.9982935,0.0002054691,0.00008592643,0.001194462,0.0001464683,0.00007421961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001991529,0.000216295,0.0001805154,0.00003215574,0.0000530397,8.287622e-7,0.00127778,0.0003665082,0.2771104,0.1591019,0.07778127,0.4838594],"study_design_scores_gemma":[0.0005373735,0.0002329572,0.0003771846,0.000009045884,0.00002084021,0.000008951378,0.0000529794,0.0540096,0.2320339,0.0600204,0.6522449,0.0004518847],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006314207,0.000008645937,0.9750851,0.01672688,0.0001876455,0.0004738663,0.000003235797,0.0006554846,0.0005449311],"genre_scores_gemma":[0.786473,0.000006516992,0.2056405,0.005538486,0.0003625349,0.001402254,0.00001536998,0.00002361638,0.0005376377],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7801588,"threshold_uncertainty_score":0.6026887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02540533101613996,"score_gpt":0.2520752218889637,"score_spread":0.2266698908728237,"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."}}