{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003003455,0.0009911328,0.001733337,0.003010557,0.000519946,0.001232909,0.0008210317,0.000930778,0.001240309],"category_scores_gemma":[0.01119642,0.0002226277,0.0006885204,0.002817815,0.0006686255,0.001108807,0.0006534564,0.001029631,0.0005941381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007881416,"about_ca_system_score_gemma":0.00063079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001212696,"about_ca_topic_score_gemma":0.0009465065,"domain_scores_codex":[0.9964089,0.001455991,0.0002554899,0.0002768158,0.001443195,0.0001596042],"domain_scores_gemma":[0.9940333,0.003392722,0.0004382259,0.0004099814,0.001618592,0.0001072633],"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":[0.0003274198,0.0001692484,0.002785082,0.0002602201,0.0001099253,0.000160049,0.00006343499,0.1788792,0.0126489,0.01480678,0.004206835,0.7855829],"study_design_scores_gemma":[0.00001905985,0.0001855299,0.001104404,0.00001806153,0.00002103182,0.0001065821,0.00002164063,0.9813733,0.005638132,0.009878521,0.001603197,0.0000305235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02632739,0.001338352,0.9702308,0.0002369559,0.00008087152,0.00007954244,0.0001073888,0.0006105875,0.0009881554],"genre_scores_gemma":[0.6283755,0.0006427483,0.3685417,0.0001075815,0.0002450895,0.0002151302,0.0004857506,0.0001315741,0.001254867],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003010557,"threshold_uncertainty_score":0.01588392,"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."}}