{"id":"W4376956106","doi":"10.1007/s10618-023-00931-x","title":"On the evaluation of outlier detection and one-class classification: a comparative study of algorithms, model selection, and ensembles","year":2023,"lang":"en","type":"article","venue":"Data Mining and Knowledge Discovery","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Danmarks Frie Forskningsfond; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Anomaly detection; Outlier; Artificial intelligence; Machine learning; One-class classification; Computer science; Class (philosophy); Selection (genetic algorithm); Pattern recognition (psychology); Support vector machine; Focus (optics); Data mining","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":[0.0008629459,0.00008340736,0.0001336964,0.0001205535,0.0002195897,0.00007812394,0.0002026646,0.00003431394,5.378078e-7],"category_scores_gemma":[0.00005868347,0.00006561433,0.000009573524,0.0004461246,0.00006702246,0.0003890769,0.0002417495,0.00006057682,7.890523e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001427951,"about_ca_system_score_gemma":0.0000648146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000250738,"about_ca_topic_score_gemma":0.0001907392,"domain_scores_codex":[0.9990701,0.0001268752,0.0002029248,0.0003472263,0.0001796799,0.0000731687],"domain_scores_gemma":[0.999032,0.0002288117,0.0001322775,0.0004188191,0.0001656079,0.00002251536],"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":[0.00009432316,0.001601022,0.002683695,0.0001328094,0.0003105285,2.790009e-7,0.0455602,0.0009770091,0.01656848,0.05696161,0.003353398,0.8717567],"study_design_scores_gemma":[0.0002371755,0.0001917079,0.009416461,0.00002389989,0.00004443982,0.000001725227,0.003345968,0.9835851,0.001585302,0.001462005,0.00003509375,0.00007114719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8858157,0.00008950975,0.1132767,0.00009201104,0.00002493556,0.0003495174,0.00003755886,0.00004907616,0.0002649139],"genre_scores_gemma":[0.9982055,0.0000455231,0.001529802,0.000005887892,0.0000147722,0.00008777652,0.00001629329,0.000004254017,0.00009016324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9826081,"threshold_uncertainty_score":0.2675675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2707064289841571,"score_gpt":0.3888650291681965,"score_spread":0.1181586001840393,"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."}}