{"id":"W1983150145","doi":"10.1109/icdm.2014.63","title":"Towards Scalable and Accurate Online Feature Selection for Big Data","year":2014,"lang":"en","type":"article","venue":"","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Feature selection; Computer science; Scalability; Big data; Benchmark (surveying); Curse of dimensionality; Feature (linguistics); Data mining; Dimensionality reduction; Selection (genetic algorithm); Artificial intelligence; Machine learning; Feature extraction; Pattern recognition (psychology); Database","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":[0.003047235,0.002183791,0.002344808,0.002577925,0.000785364,0.001815484,0.002422541,0.001168102,0.001649543],"category_scores_gemma":[0.01260637,0.0009431527,0.001308342,0.003118624,0.0007464496,0.003493312,0.001981304,0.002277242,0.001999785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005993767,"about_ca_system_score_gemma":0.001324494,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002928981,"about_ca_topic_score_gemma":0.003890415,"domain_scores_codex":[0.997242,0.0008093161,0.0001552994,0.0004477958,0.001164924,0.0001807753],"domain_scores_gemma":[0.9923404,0.004189803,0.0006043304,0.001475125,0.001172154,0.0002180699],"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":[0.0006132626,0.0004393784,0.005133365,0.0002716735,0.000297947,0.0004088507,0.0002065556,0.1887265,0.02597078,0.004917059,0.01922328,0.7537914],"study_design_scores_gemma":[0.00005398777,0.00007822759,0.0008361954,0.00001086741,0.0000201988,0.0001169682,0.00005917759,0.9831367,0.004027737,0.009304244,0.002336375,0.00001925845],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01173721,0.0006891169,0.9828382,0.0003456094,0.00006927075,0.0001098828,0.0002584644,0.003573444,0.0003788201],"genre_scores_gemma":[0.2215351,0.0005221482,0.7728868,0.0004071516,0.0003057469,0.00046884,0.002069095,0.0003554922,0.001449589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003047235,"threshold_uncertainty_score":0.01611555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06871331023400729,"score_gpt":0.2985533757496955,"score_spread":0.2298400655156882,"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."}}