{"id":"W7126268626","doi":"10.1109/mepcon66918.2026.11360210","title":"Effects of Feature Selection on Machine Learning-Based Outdoor Insulator Defects Classification","year":2025,"lang":"","type":"article","venue":"","topic":"High voltage insulation and dielectric phenomena","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pattern recognition (psychology); Feature selection; Naive Bayes classifier; Support vector machine; Preprocessor; Feature extraction; Test data; Principal component analysis","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":[0.002494007,0.001094663,0.001249599,0.0009287193,0.0004600982,0.0006472687,0.0005179183,0.000544198,0.0007325316],"category_scores_gemma":[0.005896176,0.0001425505,0.0005933831,0.0009625317,0.0003377747,0.0006109742,0.0003629967,0.0005179832,0.0003447659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002208172,"about_ca_system_score_gemma":0.0004364367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002378475,"about_ca_topic_score_gemma":0.001707632,"domain_scores_codex":[0.9986614,0.0004667534,0.000143143,0.0002131837,0.000352257,0.000163252],"domain_scores_gemma":[0.9969856,0.002077092,0.0001539708,0.0001285338,0.0005845086,0.00007021669],"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":[0.002102592,0.0009123681,0.03869873,0.000341817,0.0002603104,0.0005895232,0.000225589,0.06359633,0.04270963,0.0002799308,0.003295853,0.8469874],"study_design_scores_gemma":[0.0001068989,0.001211558,0.06745399,0.0000643067,0.0002896849,0.0006562305,0.0003475696,0.873098,0.0534973,0.0005863991,0.00262873,0.00005933976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8496096,0.002609199,0.1430803,0.0003114839,0.0002467154,0.0002008062,0.0004312998,0.001896417,0.00161419],"genre_scores_gemma":[0.9618309,0.0002477089,0.03643529,0.00005224976,0.00005251071,0.00007759329,0.0007495933,0.00005279331,0.0005013642],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002494007,"threshold_uncertainty_score":0.01318973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008080285448282237,"score_gpt":0.2470769140694655,"score_spread":0.2389966286211833,"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."}}