{"id":"W3039137796","doi":"10.1145/3394053","title":"Internal Evaluation of Unsupervised Outlier Detection","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Knowledge Discovery from Data","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Outlier; Anomaly detection; Computer science; Cluster analysis; Data mining; Artificial intelligence; Domain (mathematical analysis); Pattern recognition (psychology); Binary number; Mathematics","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":[0.02575282,0.002216955,0.002269383,0.00693881,0.001342251,0.004383758,0.00228229,0.002177985,0.001320314],"category_scores_gemma":[0.09601629,0.0003817446,0.001436239,0.004000065,0.001967856,0.003794039,0.003600796,0.001694348,0.0008051068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00151691,"about_ca_system_score_gemma":0.002161565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001395493,"about_ca_topic_score_gemma":0.001960982,"domain_scores_codex":[0.9721696,0.008948387,0.003028749,0.003417876,0.01151485,0.000920582],"domain_scores_gemma":[0.9299034,0.03028598,0.009880595,0.008466583,0.01983213,0.001631266],"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":[0.001810462,0.0009420441,0.1164282,0.001585192,0.001530322,0.0002738165,0.001127759,0.2266285,0.02338895,0.02658168,0.01352165,0.5861814],"study_design_scores_gemma":[0.0001174579,0.001581934,0.03122169,0.0002946969,0.0002887738,0.0004777281,0.0009715053,0.8807139,0.05042863,0.02639274,0.007284181,0.0002267736],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2234891,0.001834084,0.7572403,0.0006020296,0.0004099645,0.0005633871,0.001746069,0.00428494,0.009830108],"genre_scores_gemma":[0.7263756,0.0003531367,0.2666107,0.0002104416,0.0001544421,0.0003283015,0.003687615,0.000604468,0.001675188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02575282,"threshold_uncertainty_score":0.1361956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1100711224031969,"score_gpt":0.3275591643988292,"score_spread":0.2174880419956323,"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."}}