{"id":"W2151103537","doi":"10.5430/air.v2n2p77","title":"An adaptive methodology to discretize and select features","year":2013,"lang":"en","type":"article","venue":"Artificial Intelligence Research","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministerio de Economía y Competitividad","keywords":"Computer science; Feature (linguistics); Discretization; Artificial intelligence; Machine learning; Data mining; Feature selection; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001162357,0.0006996928,0.0008113566,0.001183687,0.0004760589,0.0009963964,0.001696683,0.0009240144,0.002702995],"category_scores_gemma":[0.005429482,0.0004148267,0.0007093577,0.001162377,0.000689311,0.001086025,0.001084847,0.001599019,0.0008055945],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004412327,"about_ca_system_score_gemma":0.0005610238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001344605,"about_ca_topic_score_gemma":0.001966105,"domain_scores_codex":[0.9991477,0.0001810437,0.00006602186,0.0002549411,0.0002751357,0.00007518291],"domain_scores_gemma":[0.9983729,0.000863604,0.0001425737,0.0002590303,0.0003169229,0.00004494151],"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":[0.0002598908,0.0001987717,0.002156102,0.0002227471,0.0001110835,0.0001634535,0.0003546653,0.1251141,0.08266905,0.03366131,0.004170439,0.7509186],"study_design_scores_gemma":[0.00003513307,0.000116086,0.00068644,0.00001983959,0.00002533339,0.0001602939,0.00004787626,0.9645648,0.01294939,0.01396657,0.007398907,0.00002925869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002727161,0.0000456694,0.9965599,0.00003106303,0.0000252711,0.0000338455,0.00001906002,0.0002000204,0.0003581103],"genre_scores_gemma":[0.1273428,0.00009715455,0.8697999,0.0001089981,0.00006384489,0.0002872619,0.0001630565,0.0001685573,0.001968487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002702995,"threshold_uncertainty_score":0.009042382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.335412719620955,"score_gpt":0.4778933484378903,"score_spread":0.1424806288169352,"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."}}