{"id":"W1584380365","doi":"10.1115/1.859599.paper24","title":"Using Evolvable Regressors to Partition Data","year":2010,"lang":"en","type":"book-chapter","venue":"ASME Press eBooks","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Partition (number theory); Computer science; Data set; Data mining; Process (computing); Set (abstract data type); Artificial intelligence; Mathematics; Combinatorics","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":[0.003108881,0.001104461,0.00106215,0.001254174,0.0006892899,0.001495734,0.001944556,0.001009501,0.001551419],"category_scores_gemma":[0.00812746,0.0007356773,0.001285899,0.0009742447,0.001657017,0.002404343,0.002499898,0.001959892,0.0005560266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001277628,"about_ca_system_score_gemma":0.0009202689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002800657,"about_ca_topic_score_gemma":0.003215295,"domain_scores_codex":[0.9987398,0.0005199363,0.00007326696,0.0002851637,0.0002770794,0.0001048319],"domain_scores_gemma":[0.9971278,0.001811441,0.0001942223,0.0004163568,0.0003416851,0.0001083671],"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":[0.0001156037,0.00007182937,0.00390525,0.00008486085,0.0001538767,0.0001000483,0.000529822,0.8135145,0.008997319,0.03073695,0.0008507011,0.1409393],"study_design_scores_gemma":[0.0000128207,0.00004358965,0.0002633306,0.00001878469,0.00001833757,0.00004018988,0.00004852057,0.9773313,0.003309834,0.01754975,0.001350414,0.0000130434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05237706,0.0003759306,0.9445428,0.0003017136,0.00003045387,0.00006696027,0.00006588702,0.0006323867,0.001606909],"genre_scores_gemma":[0.3919953,0.0003962048,0.6015397,0.0003213228,0.00004706191,0.0002794189,0.0004748417,0.0003965303,0.004549582],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003108881,"threshold_uncertainty_score":0.01644146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1772480937848931,"score_gpt":0.3245944538905887,"score_spread":0.1473463601056956,"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."}}