{"id":"W3132426513","doi":"10.1371/journal.pone.0246159","title":"HDSI: High dimensional selection with interactions algorithm on feature selection and testing","year":2021,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Face and Expression Recognition","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto; Princess Margaret Cancer Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Prostate Cancer Canada","keywords":"Lasso (programming language); Feature selection; Leverage (statistics); Statistical hypothesis testing; Computer science; Selection (genetic algorithm); Statistical model; Feature (linguistics); Artificial intelligence; Statistical inference; Model selection; Machine learning; Algorithm; Pattern recognition (psychology); Data mining; Mathematics; Statistics","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":[0.007514585,0.001987208,0.002685463,0.002364382,0.001102794,0.001473779,0.002652517,0.001552389,0.004871348],"category_scores_gemma":[0.01251293,0.0007155491,0.002425919,0.001966472,0.001099471,0.001326831,0.002964817,0.003304983,0.00142536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006987778,"about_ca_system_score_gemma":0.002225488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002344339,"about_ca_topic_score_gemma":0.002293841,"domain_scores_codex":[0.9935236,0.003614592,0.0003230977,0.000708506,0.001582652,0.0002474973],"domain_scores_gemma":[0.992548,0.005409343,0.000334776,0.0005664868,0.0009476876,0.0001937526],"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":[0.0007180394,0.0005144711,0.008338381,0.0004035016,0.0009603326,0.0003654934,0.0003152761,0.1929533,0.006452351,0.02397331,0.01550811,0.7494974],"study_design_scores_gemma":[0.0001082118,0.0002753854,0.001882876,0.00003436238,0.00009158452,0.0001458672,0.00005184622,0.9733099,0.002896671,0.01640142,0.004757128,0.00004464874],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003729363,0.0001932483,0.9940447,0.000136117,0.00003739246,0.0001529036,0.0001066448,0.001019464,0.000580162],"genre_scores_gemma":[0.1146904,0.0002894319,0.8788706,0.0004109854,0.0001679922,0.001413626,0.001460888,0.0003240368,0.00237201],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007514585,"threshold_uncertainty_score":0.0397414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050915829329847,"score_gpt":0.2177722235576707,"score_spread":0.1872630652643722,"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."}}