{"id":"W202723009","doi":"10.1007/978-3-540-39935-3_15","title":"Adaptive Training for Combining Classifier Ensembles","year":2004,"lang":"en","type":"book-chapter","venue":"Studies in fuzziness and soft computing","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Classifier (UML); Computer science; Random subspace method; Training set; Machine learning; Artificial intelligence; Ensemble learning; Pattern recognition (psychology); Test set; Cascading classifiers","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004257709,0.0009710731,0.001879695,0.0009408524,0.0006922534,0.001291931,0.002542813,0.00164222,0.002488497],"category_scores_gemma":[0.01294207,0.0009678306,0.001152011,0.001373969,0.000889334,0.003356148,0.002497665,0.002805438,0.000759739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005958022,"about_ca_system_score_gemma":0.0004342619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008982059,"about_ca_topic_score_gemma":0.001714125,"domain_scores_codex":[0.9979234,0.0006477205,0.0001325693,0.0004416541,0.0007348522,0.000119927],"domain_scores_gemma":[0.9948666,0.003077084,0.0001904609,0.001038605,0.0007477982,0.00007945333],"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":[0.00012915,0.00009967604,0.00101719,0.0001149768,0.0002947555,0.00009068097,0.0002345095,0.3410467,0.008144622,0.03826057,0.003395428,0.6071718],"study_design_scores_gemma":[0.000006950983,0.00003481958,0.0001970232,0.00001440637,0.0000375417,0.00005431523,0.00001194075,0.9707767,0.002266758,0.02520912,0.001379014,0.00001128601],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0060841,0.0003557159,0.9917957,0.00008871865,0.00005954602,0.00003381337,0.00001555303,0.0003206074,0.001246218],"genre_scores_gemma":[0.260143,0.0004850132,0.7335515,0.0002013694,0.0002657714,0.0002488815,0.000204344,0.0002067371,0.004693423],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004257709,"threshold_uncertainty_score":0.0225172,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1510094949050544,"score_gpt":0.3393871998367348,"score_spread":0.1883777049316805,"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."}}