{"id":"W2617645851","doi":"10.1007/978-3-319-59876-5_45","title":"Classification Boosting by Data Decomposition Using Consensus-Based Combination of Classifiers","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Statistical and Computational Modeling","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Boosting (machine learning); Classifier (UML); Decomposition; Artificial intelligence; Generalization; Machine learning; Data mining; Pattern recognition (psychology); 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008960674,0.0003363916,0.0004141857,0.0004549162,0.0004395016,0.0004228793,0.003350848,0.0002233211,0.00000466882],"category_scores_gemma":[0.000285484,0.000345176,0.00006115165,0.0001868525,0.0007453277,0.0005141086,0.0008723644,0.000423772,0.000004981805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002500245,"about_ca_system_score_gemma":0.0007857773,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004559787,"about_ca_topic_score_gemma":0.00001211428,"domain_scores_codex":[0.9966453,0.00005363686,0.0006610496,0.001300578,0.0009832558,0.0003561509],"domain_scores_gemma":[0.9958227,0.001036732,0.0008224315,0.001687714,0.0005133342,0.0001171383],"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":[0.00002309638,0.0001105281,0.0001511494,0.0001258749,0.00002343708,0.00002610172,0.000143906,0.1803944,0.004095431,0.08447992,0.00007756439,0.7303485],"study_design_scores_gemma":[0.0002700315,0.00006671655,0.0001541522,0.0003930145,0.00001310411,0.00001340911,1.239685e-7,0.8691189,0.000602405,0.1290096,0.00005116147,0.0003073087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003134844,0.0001545928,0.9970047,0.0007132848,0.0007352687,0.0002724002,0.00006675699,0.00006626957,0.0006731864],"genre_scores_gemma":[0.4842756,0.000003860286,0.515264,0.0002398162,0.00007356072,0.000001805009,0.0001082685,0.00001565113,0.00001742201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7300412,"threshold_uncertainty_score":0.9999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09257301002347595,"score_gpt":0.3379655773033772,"score_spread":0.2453925672799012,"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."}}