{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002082124,0.0002135623,0.0002139941,0.0001897946,0.0004691993,0.0003255054,0.001365067,0.0003025729,0.00005090048],"category_scores_gemma":[0.00004482807,0.0002457017,0.0001057636,0.0002007455,0.0000422307,0.0006975161,0.001374474,0.0004662825,0.0001704085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001040346,"about_ca_system_score_gemma":0.0002029393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000261016,"about_ca_topic_score_gemma":0.00002233008,"domain_scores_codex":[0.998605,0.00009276869,0.000118866,0.0008264207,0.00007682676,0.0002800897],"domain_scores_gemma":[0.9985018,0.00004992646,0.0002723882,0.0008940691,0.0001709187,0.000110875],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008048794,0.0002980961,0.004990279,0.0003692747,0.0002767151,0.0003716153,0.0006936966,0.4150208,0.004818454,0.5483195,0.01396277,0.0107984],"study_design_scores_gemma":[0.0004012802,0.0000436512,0.0004866383,0.0003751491,0.00005484597,0.000009817432,0.00003553393,0.8090749,0.00237888,0.180805,0.005785671,0.0005487245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07777668,0.00003231853,0.9047746,0.00008104387,0.0008424279,0.0001742188,0.000005538902,0.0003456717,0.01596751],"genre_scores_gemma":[0.9815291,0.000107273,0.01281726,0.00006381172,0.00009722039,0.000001218708,0.00001642852,0.00001231015,0.005355378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9037524,"threshold_uncertainty_score":0.9999995,"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."}}