{"id":"W2108587684","doi":"10.1109/icsmc.2007.4414009","title":"Multi-objective competitive coevolution for efficient GP classifier problem decomposition","year":2007,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Scalability; Genetic programming; Classifier (UML); Artificial intelligence; Pareto principle; Machine learning; Binary classification; Theoretical computer science; Mathematical optimization; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001955525,0.0007790482,0.001090828,0.000713319,0.0004283101,0.001020301,0.001249907,0.001397042,0.001482853],"category_scores_gemma":[0.005804271,0.0004041107,0.0005559871,0.00102703,0.0007974508,0.0008170354,0.001384632,0.001605073,0.0003631873],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008528904,"about_ca_system_score_gemma":0.001113062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003316181,"about_ca_topic_score_gemma":0.002822209,"domain_scores_codex":[0.9992952,0.0002939532,0.00003230602,0.00009079312,0.0002102228,0.0000774328],"domain_scores_gemma":[0.9985391,0.0009617771,0.00007020374,0.0001173794,0.000260947,0.00005059625],"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":[0.00003097214,0.00004645263,0.0004396586,0.0000497118,0.0000344609,0.00008140532,0.000104906,0.8862105,0.003157082,0.02491887,0.001281751,0.08364423],"study_design_scores_gemma":[0.000002600514,0.000004976318,0.00002119788,0.000001599529,0.000001744994,0.000006974406,0.000004211899,0.9974541,0.0001868746,0.002095654,0.0002188825,0.0000011007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01340993,0.0001214478,0.9840345,0.0001563333,0.00001836511,0.00004071113,0.00001109247,0.0001597137,0.002047751],"genre_scores_gemma":[0.3864428,0.0002274514,0.6094721,0.0002001683,0.00005018838,0.0004066397,0.0001343894,0.0001112646,0.002955014],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003316181,"threshold_uncertainty_score":0.01034194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0167069571616044,"score_gpt":0.291026138436937,"score_spread":0.2743191812753326,"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."}}