{"id":"W2157052607","doi":"10.1109/nafips.2007.383865","title":"Biomedical Spectral Classification Using Stochastic Feature Selection and Fuzzy Aggregation","year":2007,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Manitoba; National Research Council Canada","funders":"","keywords":"Feature selection; Curse of dimensionality; Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Computer science; Fuzzy logic; Feature (linguistics); Feature vector; Machine learning; Data mining; Mathematics","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":[0.002562834,0.0005507909,0.001081564,0.001254473,0.0005104005,0.0009739088,0.0004935467,0.000536597,0.0003678164],"category_scores_gemma":[0.004459518,0.0002563494,0.0008062434,0.0009212181,0.0004656654,0.0006592624,0.0007390234,0.0003953805,0.0001241842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007118237,"about_ca_system_score_gemma":0.0005337455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002105941,"about_ca_topic_score_gemma":0.001889516,"domain_scores_codex":[0.99871,0.0003885581,0.00009888854,0.0001671577,0.0005480702,0.00008725934],"domain_scores_gemma":[0.9985582,0.0006939379,0.0001744719,0.0001353033,0.0003936421,0.0000443818],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003504999,0.000144771,0.003108205,0.00007514877,0.0001828747,0.0001390224,0.0001239105,0.6287568,0.02850102,0.009588762,0.0009914004,0.3280376],"study_design_scores_gemma":[0.000007275884,0.00005162276,0.0007431579,0.000003644521,0.00001521537,0.00002574273,0.000007770486,0.9928747,0.002888735,0.003166134,0.0002064696,0.000009548039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0696582,0.0002205739,0.9287322,0.0001382776,0.00003345754,0.00005206655,0.00003314738,0.0002935344,0.0008385982],"genre_scores_gemma":[0.7536408,0.000106894,0.2454148,0.0000682255,0.00006717147,0.00009561415,0.00009985611,0.00002044605,0.0004861781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002562834,"threshold_uncertainty_score":0.01355374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0117675218049149,"score_gpt":0.2392248947954497,"score_spread":0.2274573729905348,"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."}}