{"id":"W4248531488","doi":"10.22215/etd/2017-11732","title":"An Adaptive and Diversity-Based Ensemble Method for Binary Classification","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Ensemble learning; Computer science; Machine learning; Artificial intelligence; Binary number; Construct (python library); Ensemble forecasting; Stacking; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005921895,0.0001994425,0.0002120353,0.0001934609,0.0009895877,0.000337197,0.0009868414,0.0002323074,0.000008194162],"category_scores_gemma":[0.00009683592,0.0001907006,0.00006005972,0.00007239914,0.00001990792,0.0006115207,0.00006689903,0.0001800038,0.00001034393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003402965,"about_ca_system_score_gemma":0.0001436163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002008266,"about_ca_topic_score_gemma":0.0002510792,"domain_scores_codex":[0.9985794,0.0001449101,0.000166723,0.0007286046,0.0002132417,0.0001671371],"domain_scores_gemma":[0.9980841,0.0001928955,0.0003861189,0.001027129,0.0002080423,0.000101717],"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":[0.0002625042,0.0001542195,0.0003864055,0.0001915172,0.00004050354,0.000002284843,0.001071399,0.00005464985,0.01024462,0.2129589,0.003972614,0.7706603],"study_design_scores_gemma":[0.0004180178,0.0004186222,0.03578327,0.00004196827,0.00005285865,9.744905e-7,0.0002427209,0.9485483,0.001027117,0.003111957,0.01000518,0.0003489866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0009102533,0.00005619827,0.9855438,0.0005458631,0.0003147254,0.0003861079,0.00002514024,0.0001922844,0.01202562],"genre_scores_gemma":[0.2892007,0.00002546667,0.6957061,0.0001912004,0.0001035526,0.000105718,0.004422769,0.00002665411,0.01021787],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9484937,"threshold_uncertainty_score":0.7776546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05905510040640796,"score_gpt":0.3596577143543701,"score_spread":0.3006026139479621,"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."}}