{"id":"W2887269903","doi":"10.1109/tcyb.2018.2859342","title":"Multiple Relevant Feature Ensemble Selection Based on Multilayer Co-Evolutionary Consensus MapReduce","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Cybernetics","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Australian Research Council; Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Feature selection; Computer science; Selection (genetic algorithm); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Machine learning","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":[0.0001553755,0.0002975851,0.000208186,0.0002234676,0.0005211818,0.0001057661,0.0004228904,0.0002468166,0.00006040982],"category_scores_gemma":[0.00001848594,0.0002745635,0.0001442436,0.000511101,0.0001700252,0.00009970889,0.000002976491,0.0004806152,0.0004830308],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001791292,"about_ca_system_score_gemma":0.0001479883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006464037,"about_ca_topic_score_gemma":0.0000929628,"domain_scores_codex":[0.9980163,0.0001396034,0.0002591863,0.0006517101,0.0004730825,0.0004601055],"domain_scores_gemma":[0.9985019,0.0003554661,0.00009712662,0.0006575371,0.000214837,0.0001731534],"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":[0.001600915,0.005689687,0.0005954908,0.0001021554,0.0002824063,0.0001602794,0.002544879,0.531037,0.0541627,0.002901928,0.1481741,0.2527484],"study_design_scores_gemma":[0.001092047,0.001076442,0.001415527,0.00004511723,0.00002466846,0.00005394898,0.00002100317,0.9210044,0.05870532,0.0002749058,0.01588943,0.0003972269],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01613226,0.00002765621,0.9734215,0.002215643,0.001772772,0.0004251401,0.00005673003,0.0004605205,0.005487785],"genre_scores_gemma":[0.9007719,0.00001614597,0.09679332,0.0009311817,0.0001661116,0.00003074746,0.0000047069,0.00002804023,0.001257804],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8846397,"threshold_uncertainty_score":0.9999707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0200130637100457,"score_gpt":0.2518735351632771,"score_spread":0.2318604714532314,"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."}}