{"id":"W2158704884","doi":"10.1177/0954406212469757","title":"Feature ranking for support vector machine classification and its application to machinery fault diagnosis","year":2012,"lang":"en","type":"article","venue":"Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Support vector machine; Ranking (information retrieval); Artificial intelligence; Feature (linguistics); Feature vector; Pattern recognition (psychology); Benchmark (surveying); Computer science; Sorting; Ranking SVM; Class (philosophy); Similarity (geometry); Kernel (algebra); Machine learning; Kernel method; Fault (geology); Data mining; Mathematics; Algorithm","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":[0.00184984,0.0002130892,0.0004098524,0.0002961637,0.00009468084,0.00002994128,0.0005421407,0.0001463708,0.00000289407],"category_scores_gemma":[0.001380405,0.0001664495,0.000168584,0.000795109,0.00004505926,0.0005867992,0.0000666657,0.0003373391,0.000001137778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000162215,"about_ca_system_score_gemma":0.0000439923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001157208,"about_ca_topic_score_gemma":3.070687e-7,"domain_scores_codex":[0.998172,0.000005892883,0.0006380667,0.0001825635,0.0006428473,0.0003586604],"domain_scores_gemma":[0.9987413,0.0001013933,0.0002848402,0.000119075,0.0004466338,0.0003067389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005313888,0.00004986507,0.00003265117,0.0003142555,0.00004562981,9.424465e-8,0.00009838988,0.04609619,0.893059,0.05825258,0.0001409765,0.001857218],"study_design_scores_gemma":[0.0006058222,0.0001813189,0.0002587622,0.0002687206,0.00007751562,0.00006369701,0.00005042072,0.5789148,0.4143022,0.00006295465,0.005008741,0.0002050146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5887701,0.001035845,0.4017986,0.00163127,0.004866264,0.00154803,0.00005228411,0.0002055621,0.00009205288],"genre_scores_gemma":[0.9959612,0.00006166819,0.00359682,0.00002863974,0.0002452627,0.00007371128,6.754503e-7,0.00002362303,0.000008365228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5328186,"threshold_uncertainty_score":0.6787613,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0120845641240551,"score_gpt":0.2353090473375356,"score_spread":0.2232244832134805,"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."}}