{"id":"W2139642264","doi":"10.1109/iwfhr.2004.105","title":"Unsupervised Feature Selection for Ensemble of Classifiers","year":2004,"lang":"en","type":"article","venue":"","topic":"Advanced Multi-Objective Optimization Algorithms","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Random subspace method; Computer science; Artificial intelligence; Hidden Markov model; Feature selection; Pattern recognition (psychology); Feature (linguistics); Machine learning; Context (archaeology); Selection (genetic algorithm); Set (abstract data type); Cascading classifiers; Ensemble learning; Support vector machine","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":[0.00006666225,0.00008615873,0.0001083522,0.00008666323,0.00007333952,0.00002319073,0.0002233849,0.00006186478,0.000005662682],"category_scores_gemma":[0.00005095414,0.00007835935,0.0000540993,0.0004460742,0.0000233239,0.0003839533,0.00003407724,0.00005837705,0.000004252062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008551314,"about_ca_system_score_gemma":0.00008838912,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001107123,"about_ca_topic_score_gemma":0.00001600106,"domain_scores_codex":[0.999357,0.00001148041,0.0001126967,0.0002438838,0.000119326,0.000155539],"domain_scores_gemma":[0.999415,0.00004071628,0.00006063173,0.0001778511,0.0002580477,0.00004768477],"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":[0.000058229,0.0003210009,0.0003025111,0.00005447467,0.00006578786,0.000001569584,0.001304532,0.4768015,0.06384686,0.381589,0.00133256,0.07432197],"study_design_scores_gemma":[0.002962092,0.0002907802,0.0006085051,0.00001741658,0.000007904119,0.00001542415,0.0001143094,0.6946645,0.2808881,0.01790589,0.002246984,0.000278097],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004859906,0.00001553935,0.9964529,0.0007107488,0.0001578804,0.0002782348,0.000001500224,0.0001461896,0.001750973],"genre_scores_gemma":[0.08459514,0.00000552055,0.9140049,0.0001823913,0.00002449012,0.00002322066,0.000002769769,0.000008368786,0.00115316],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3636831,"threshold_uncertainty_score":0.3195401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01541284096524292,"score_gpt":0.2631135485831697,"score_spread":0.2477007076179268,"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."}}