{"id":"W2516818401","doi":"10.1016/j.neucom.2016.08.011","title":"A double-layer ELM with added feature selection ability using a sparse Bayesian approach","year":2016,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Machine Learning and ELM","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Feature selection; Computer science; Pattern recognition (psychology); Layer (electronics); Bayesian probability; Prior probability; Artificial intelligence; Feature (linguistics); Benchmark (surveying); Pruning; Extreme learning machine; Classifier (UML); Gaussian; Generalization; Algorithm; Machine learning; Mathematics; Artificial neural network","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001086051,0.0009656547,0.001348253,0.0006094629,0.0005364321,0.0009022054,0.002105676,0.002296812,0.004502674],"category_scores_gemma":[0.001623663,0.0008271841,0.001129949,0.0008665278,0.0003410162,0.001658009,0.001452748,0.001599902,0.002155188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004274845,"about_ca_system_score_gemma":0.001096996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00433465,"about_ca_topic_score_gemma":0.006278143,"domain_scores_codex":[0.9996049,0.00008356838,0.00003416223,0.00009636051,0.0001210636,0.00005999719],"domain_scores_gemma":[0.999529,0.0001566279,0.00002480677,0.00005654381,0.000196116,0.00003696518],"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":[0.0003795725,0.0002843528,0.000866453,0.0001992309,0.0003128959,0.0002018828,0.00009401461,0.3737604,0.02215822,0.007427415,0.009980673,0.5843349],"study_design_scores_gemma":[0.00001423764,0.00002164807,0.00008663814,0.000005666918,0.00001861717,0.00002898153,0.000004447578,0.996163,0.002014131,0.0009800624,0.0006525274,0.00001011137],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004738802,0.0001822716,0.9931204,0.0001443172,0.00008966748,0.00002902242,0.00006374702,0.0006892684,0.0009425603],"genre_scores_gemma":[0.1752749,0.0002404358,0.8148766,0.0005076176,0.0001389552,0.0001696506,0.0004513906,0.0001910209,0.008149453],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004502674,"threshold_uncertainty_score":0.01506299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0245839175093784,"score_gpt":0.2536767318904419,"score_spread":0.2290928143810635,"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."}}