{"id":"W2934148436","doi":"10.5555/3121409.3121410","title":"Feature Selection for Classification under Anonymity Constraint","year":2017,"lang":"en","type":"book-chapter","venue":"IUScholarWorks (Indiana University)","topic":"Privacy-Preserving Technologies in Data","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Microdata (statistics); Computer science; Anonymity; Feature selection; Differential privacy; Data mining; Information retrieval; Feature (linguistics); Data anonymization; Information privacy; Machine learning; Internet privacy; Computer security","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":[0.002553592,0.001232396,0.002372357,0.002012772,0.001006862,0.001693678,0.001995038,0.001296003,0.003148639],"category_scores_gemma":[0.006640421,0.0003108321,0.001437785,0.003325074,0.0007747315,0.001627474,0.001114288,0.00153843,0.001388087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007941138,"about_ca_system_score_gemma":0.001069197,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001414332,"about_ca_topic_score_gemma":0.0009096191,"domain_scores_codex":[0.9974715,0.0008180539,0.0001703838,0.0006784884,0.0005889334,0.0002726598],"domain_scores_gemma":[0.996962,0.001920239,0.0002180233,0.0003628976,0.0004517853,0.00008511022],"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":[0.0006775568,0.0003870095,0.005173907,0.0004155847,0.0002403798,0.000473059,0.0002140877,0.1032639,0.007350158,0.01734911,0.02989672,0.8345587],"study_design_scores_gemma":[0.00006067117,0.0001632185,0.001913879,0.00003948713,0.00006167147,0.0003373417,0.00009165146,0.9458462,0.003080796,0.04278639,0.005582533,0.00003613307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03278185,0.001666805,0.9607042,0.0008530068,0.0001647649,0.0001245248,0.0008244461,0.001004347,0.001876033],"genre_scores_gemma":[0.6573014,0.001590171,0.3247981,0.000656471,0.001265283,0.001037404,0.005840887,0.0002043487,0.007305926],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003148639,"threshold_uncertainty_score":0.01350486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04557168757880396,"score_gpt":0.2520393713130558,"score_spread":0.2064676837342518,"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."}}