{"id":"W2610563132","doi":"10.1007/978-3-319-59041-7_14","title":"A New Scalable and Performance-Enhancing Bootstrap Aggregating Scheme for Variables Selection","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in business information processing","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Computer science; Scalability; Feature selection; Generalization; Selection (genetic algorithm); Relevance (law); Machine learning; Data mining; Artificial intelligence; Predictive power; Scheme (mathematics); Model selection; Mathematics; Database","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0003370947,0.000326597,0.0003222801,0.0003446078,0.0007623635,0.001572027,0.000533886,0.0003271039,0.000009078623],"category_scores_gemma":[0.0002792917,0.0003221356,0.00003242045,0.0001752754,0.00004793501,0.005009745,0.0001787883,0.0003341452,0.000007000337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009857568,"about_ca_system_score_gemma":0.0005771882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004372256,"about_ca_topic_score_gemma":0.00004195647,"domain_scores_codex":[0.9985291,0.000003710614,0.0005608989,0.0003542917,0.0002469967,0.0003050497],"domain_scores_gemma":[0.998192,0.00009712894,0.0008327813,0.0003625419,0.000448411,0.00006714998],"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":[0.000006431858,0.000003105782,0.00003979248,0.0006995998,0.000007815361,1.688571e-7,0.0004236896,0.0007728898,0.00002795723,0.00430007,0.00005469443,0.9936638],"study_design_scores_gemma":[0.0007134307,0.00004616215,0.0003432292,0.003893923,0.00003282862,0.00005319333,0.000006394901,0.922664,0.001153721,0.02157872,0.04873994,0.0007744817],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0000577637,0.0003526836,0.9899758,0.0004010719,0.0001627119,0.0003921458,0.00001138034,0.0001524494,0.008494035],"genre_scores_gemma":[0.01339187,0.0002006548,0.9832171,0.0002723123,0.0003947777,0.00009053556,0.0002213756,0.00004041801,0.002170956],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9928893,"threshold_uncertainty_score":0.9999231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02038024116382591,"score_gpt":0.2553216788253208,"score_spread":0.2349414376614949,"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."}}