{"id":"W4316673091","doi":"10.18280/ria.360603","title":"Classification Predictive Maintenance Using XGboost with Genetic Algorithm","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crossover; Support vector machine; AdaBoost; Algorithm; Computer science; Genetic algorithm; Machine learning; Artificial intelligence; Classifier (UML); Statistical classification; Selection (genetic algorithm)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001365439,0.0008365482,0.001486255,0.001797832,0.0007536606,0.001060792,0.001299276,0.001450674,0.001515827],"category_scores_gemma":[0.001758586,0.0004902999,0.0008881421,0.001434357,0.0006027573,0.0006858357,0.0004677584,0.0009346918,0.000311877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001245023,"about_ca_system_score_gemma":0.001457024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01794701,"about_ca_topic_score_gemma":0.007911973,"domain_scores_codex":[0.9994941,0.0001204222,0.00002920874,0.0001178593,0.0001527942,0.00008572948],"domain_scores_gemma":[0.999485,0.0002505019,0.0000667538,0.00002362552,0.0001521728,0.00002192056],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001150015,0.0001252467,0.001545767,0.00005908202,0.00008163698,0.00006052442,0.00004451968,0.8643678,0.001313269,0.001735374,0.001130495,0.1294212],"study_design_scores_gemma":[0.000008476608,0.00002081894,0.0001930329,0.000005567669,0.00000656344,0.000007779277,0.000004592388,0.9987959,0.0002688256,0.0004864987,0.0001988996,0.000003085242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0733787,0.001045671,0.9185347,0.0003681035,0.0002034673,0.0001982104,0.00009240557,0.002015927,0.004162869],"genre_scores_gemma":[0.79585,0.0003545113,0.1988851,0.0002508449,0.00007748264,0.0003169604,0.0002329018,0.00009793304,0.003934139],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01794701,"threshold_uncertainty_score":0.03568512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02199844453700236,"score_gpt":0.2284423905518343,"score_spread":0.2064439460148319,"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."}}