{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001477659,0.0001412939,0.0001520659,0.00009877171,0.0002634012,0.00003650936,0.0001850852,0.00003417737,0.0002294741],"category_scores_gemma":[0.00000878614,0.0001425619,0.00005050746,0.0004457313,0.00004490322,0.00007579254,0.00003019305,0.0002279758,0.00007968511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002407087,"about_ca_system_score_gemma":0.00002375459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003532185,"about_ca_topic_score_gemma":0.000006184463,"domain_scores_codex":[0.9989378,0.00005171854,0.0002928045,0.0002568033,0.0001937005,0.0002671733],"domain_scores_gemma":[0.9994832,0.00003270117,0.00006096861,0.000303808,0.00005297472,0.00006639713],"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":[0.00001730743,0.00002538862,0.00008898797,0.00001932885,0.00002424137,0.00001332019,0.0004777303,0.9585606,0.007268043,0.0001157186,0.0001689221,0.03322041],"study_design_scores_gemma":[0.00005521881,0.000122145,0.0001440897,0.00002451103,0.0000139804,0.0001538881,0.003190553,0.9804917,0.003480538,0.00006311487,0.01208091,0.0001793656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05750138,0.0004120157,0.9371909,0.00005672629,0.000703944,0.0004163173,0.00002997437,0.000324572,0.003364097],"genre_scores_gemma":[0.9967428,0.00002357673,0.002053355,0.00003197784,0.0001014851,0.0001634138,0.000005796816,0.0000383999,0.0008391841],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9392414,"threshold_uncertainty_score":0.5813504,"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."}}