{"id":"W2166470369","doi":"10.1109/iembs.1994.415467","title":"Information based feature selection for supervised motor unit action potential classification","year":2002,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Linear discriminant analysis; Discriminative model; Computer science; Principal component analysis; Cluster analysis; Classifier (UML); Discriminant; Linear classifier; Feature selection; Statistical classification; Motor unit; Dimensionality reduction; Feature extraction; Machine learning","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":[0.001386684,0.0006698227,0.001256679,0.001688114,0.0004143021,0.0005615272,0.0006156446,0.0006047918,0.001228653],"category_scores_gemma":[0.004163094,0.0002737827,0.0006340861,0.001708778,0.0003463046,0.0005376395,0.0003594468,0.0006313683,0.0004870567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004169704,"about_ca_system_score_gemma":0.0006527571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001951237,"about_ca_topic_score_gemma":0.001760218,"domain_scores_codex":[0.9991481,0.0003114422,0.0000667455,0.0001185192,0.0002886455,0.00006664804],"domain_scores_gemma":[0.9982283,0.001046352,0.0001181179,0.0001090031,0.0004576857,0.00004061297],"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":[0.0005880232,0.000232002,0.002449952,0.0001612861,0.0001301019,0.0001987233,0.00009895307,0.1214175,0.02331331,0.002596679,0.004293419,0.84452],"study_design_scores_gemma":[0.00004195382,0.0001382206,0.003301686,0.00001328676,0.00003487772,0.00007157496,0.00001846648,0.9825813,0.008952235,0.003610463,0.001210599,0.00002534426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07788987,0.0006481239,0.9183145,0.0001758528,0.0000527591,0.0001212058,0.0004071507,0.001502997,0.0008875246],"genre_scores_gemma":[0.5745604,0.0002691213,0.4219494,0.00005983936,0.0001196566,0.0004270912,0.001371509,0.0001039754,0.001139104],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001951237,"threshold_uncertainty_score":0.007333577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04196474014968908,"score_gpt":0.2353010102375752,"score_spread":0.1933362700878862,"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."}}