{"id":"W2025458325","doi":"10.1145/1143997.1144159","title":"Improving GP classifier generalization using a cluster separation metric","year":2006,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Genetic programming; Computer science; Classifier (UML); Artificial intelligence; Machine learning; Cluster analysis; Fitness function; Maximization; Generalization; Genetic algorithm; Mathematical optimization; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002336913,0.0008626123,0.001174598,0.0009732349,0.0005672203,0.00112358,0.001251552,0.002001678,0.00113908],"category_scores_gemma":[0.009698414,0.0002881805,0.0006564376,0.001243946,0.0007577636,0.001474311,0.001452896,0.001892006,0.0005125238],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009717247,"about_ca_system_score_gemma":0.001048704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003116337,"about_ca_topic_score_gemma":0.002395804,"domain_scores_codex":[0.9991087,0.0002520363,0.0000446357,0.0002351667,0.0002726517,0.00008675349],"domain_scores_gemma":[0.9972982,0.001411595,0.0001961785,0.0004279968,0.0005939393,0.00007212517],"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":[0.0001085176,0.0001532153,0.002237313,0.00006853854,0.0001035755,0.0001123218,0.0001670175,0.7072709,0.01062341,0.01332389,0.0035413,0.2622901],"study_design_scores_gemma":[0.000007704349,0.00002449955,0.0003165334,0.000004919336,0.00001056972,0.00002494248,0.00001215182,0.9940392,0.001348742,0.003862657,0.0003436162,0.000004391778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08876244,0.0002606571,0.9052854,0.0005880013,0.00005056192,0.00007634747,0.00006471207,0.0009721661,0.003939618],"genre_scores_gemma":[0.717432,0.0001960176,0.2788682,0.0002984929,0.00006625845,0.0001597085,0.0002674064,0.0002417027,0.002470227],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003116337,"threshold_uncertainty_score":0.01235896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01961186353135251,"score_gpt":0.2727744033436832,"score_spread":0.2531625398123307,"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."}}