{"id":"W2123885828","doi":"10.1109/icsmc.2007.4413999","title":"One-class learning with multi-objective genetic programming","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"One-class classification; Artificial intelligence; Support vector machine; Computer science; Classifier (UML); Novelty detection; Machine learning; Genetic programming; Class (philosophy); Pattern recognition (psychology); Binary classification; Multiclass classification; Novelty; Data mining","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":[0.0001555481,0.00007269537,0.00006305209,0.00005454624,0.0002212091,0.00006168176,0.0002609538,0.00002828929,0.00000707316],"category_scores_gemma":[0.000007420284,0.00006303905,0.000021028,0.0004054227,0.00004026547,0.0001763371,0.00007989818,0.0001268539,0.00005225623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003619996,"about_ca_system_score_gemma":0.00003778224,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005550003,"about_ca_topic_score_gemma":0.00006705411,"domain_scores_codex":[0.9992037,0.00001366144,0.0001097185,0.0002694658,0.0001585244,0.0002449183],"domain_scores_gemma":[0.9995396,0.00005227497,0.00004301711,0.0002024349,0.00008594828,0.00007678637],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000006876953,0.0005122271,0.01592955,0.000009250773,0.00004536525,0.00001963848,0.001344436,0.002670833,0.001118812,0.09241971,0.00004763946,0.8858756],"study_design_scores_gemma":[0.001003757,0.0004870924,0.5307,0.0000302769,0.00001443075,0.00008489736,0.0006838001,0.4332044,0.00298568,0.0009804213,0.02923813,0.0005871709],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01162726,0.00004289655,0.9840074,0.0002285777,0.00002141746,0.0001793046,9.729045e-8,0.0002579795,0.003635064],"genre_scores_gemma":[0.384636,0.000001818674,0.6143833,0.00004476888,0.00002931471,0.00001914923,6.561386e-7,0.000004314908,0.0008806345],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8852885,"threshold_uncertainty_score":0.2570658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01612329271846882,"score_gpt":0.2533175292702368,"score_spread":0.237194236551768,"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."}}