{"id":"W2610476332","doi":"10.48550/arxiv.1704.08265","title":"Pruning variable selection ensembles","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Key Research and Development Program of China; China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Benchmark (surveying); Ensemble learning; Sorting; Selection (genetic algorithm); Pruning; Stability (learning theory); Context (archaeology); Artificial intelligence; Machine learning; Feature selection; Process (computing); Variable (mathematics); Pattern recognition (psychology); Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004118303,0.001290205,0.002076883,0.002377626,0.001013793,0.001190441,0.001752953,0.001096919,0.001612076],"category_scores_gemma":[0.01288916,0.0004380364,0.001078351,0.002016056,0.000658958,0.001391875,0.00213661,0.001223068,0.0006905978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005023985,"about_ca_system_score_gemma":0.001134105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001522282,"about_ca_topic_score_gemma":0.002587098,"domain_scores_codex":[0.9966238,0.001291016,0.0001768954,0.0005860566,0.001091014,0.0002312826],"domain_scores_gemma":[0.9945889,0.003114049,0.0003219616,0.0007297418,0.001103724,0.0001416212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002239129,0.0001329045,0.008222218,0.0002029135,0.0003860995,0.0002884908,0.0002200205,0.3949905,0.01086371,0.01962087,0.00647336,0.558375],"study_design_scores_gemma":[0.00001726591,0.00009755066,0.000958205,0.00003168474,0.00006567041,0.0001376825,0.00004045416,0.9781506,0.004350838,0.01289739,0.003236349,0.00001638129],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03247592,0.0008791441,0.9637411,0.0001447816,0.00008837713,0.00008559976,0.0001271715,0.000556078,0.001901798],"genre_scores_gemma":[0.5466523,0.0009156793,0.4459225,0.0004259017,0.0002438829,0.0004582804,0.001512582,0.0002218443,0.003647015],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004118303,"threshold_uncertainty_score":0.02177995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08180838361224178,"score_gpt":0.1917714408469543,"score_spread":0.1099630572347125,"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."}}