{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00107886,0.00076911,0.001181128,0.0005703959,0.0008461197,0.0006218911,0.001563694,0.0006337557,0.0009197771],"category_scores_gemma":[0.002247059,0.0002859759,0.0008793498,0.0005908618,0.0004611471,0.0008256716,0.001389748,0.0006955741,0.0001700732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005047105,"about_ca_system_score_gemma":0.001040731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004324089,"about_ca_topic_score_gemma":0.003865559,"domain_scores_codex":[0.9992474,0.0001692325,0.00003281507,0.0001692443,0.0002604929,0.0001208093],"domain_scores_gemma":[0.9992276,0.0002542913,0.00006079991,0.0001139811,0.0002675831,0.00007581066],"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":[0.000100147,0.0001067563,0.002313862,0.00008159102,0.0001371811,0.0002435094,0.00021599,0.8371716,0.009240781,0.01144493,0.002588199,0.1363556],"study_design_scores_gemma":[0.000007719381,0.00002526072,0.0001473056,0.000001664159,0.000009456962,0.00002614762,0.00002027652,0.9946194,0.000913363,0.003826657,0.0003974893,0.000005241572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.05357213,0.0001694577,0.9435154,0.0002125085,0.0000489946,0.00006420074,0.00004044787,0.0003901036,0.001986831],"genre_scores_gemma":[0.8457689,0.0001098402,0.1511298,0.0001654784,0.00004722376,0.0002190458,0.0001622785,0.00006203399,0.002335352],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004324089,"threshold_uncertainty_score":0.008597791,"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."}}