{"id":"W2212891330","doi":"10.1007/s41060-018-0161-7","title":"Spectral ranking and unsupervised feature selection for point, collective, and contextual anomaly detection","year":2018,"lang":"en","type":"article","venue":"International Journal of Data Science and Analytics","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Feature selection; Ranking (information retrieval); Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Computer science; Point (geometry); Anomaly (physics); Selection (genetic algorithm); Machine learning; Data mining; Mathematics; Physics; Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008181686,0.00007703187,0.0001069089,0.0003209407,0.0003688577,0.0004636943,0.0006925369,0.00003552439,0.0000020021],"category_scores_gemma":[0.0001697432,0.00006614563,0.00001963607,0.0004629474,0.0003548201,0.002150467,0.0002423654,0.0001042358,2.51276e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007183621,"about_ca_system_score_gemma":0.0001852934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001635208,"about_ca_topic_score_gemma":0.00005325321,"domain_scores_codex":[0.999059,0.00001292721,0.0001975764,0.0002760756,0.0003334131,0.0001210624],"domain_scores_gemma":[0.9984183,0.00007029474,0.0001897928,0.0001388578,0.001098416,0.000084331],"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":[0.0003728135,0.0002131037,0.01375117,0.00002781986,0.0003124611,0.00001141652,0.001735319,0.00001525285,0.208609,0.05474149,0.004020802,0.7161893],"study_design_scores_gemma":[0.001775315,0.001497472,0.02529319,0.00007842285,0.00007850025,0.001897809,0.0004165907,0.8852018,0.05072303,0.01535224,0.01733571,0.000349915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1589325,0.00007176547,0.8386636,0.001898447,0.0001594432,0.0001051954,0.00001953324,0.00001934759,0.0001301594],"genre_scores_gemma":[0.952659,0.0001272411,0.04668503,0.0002088926,0.0002604692,0.000001633407,0.000001355666,0.000003169685,0.00005318006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8851866,"threshold_uncertainty_score":0.4471415,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03377197851990105,"score_gpt":0.3192722043912278,"score_spread":0.2855002258713268,"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."}}