{"id":"W2462867792","doi":"10.1007/s13748-016-0096-y","title":"Applying multi-label and multi-class classification to enhance K-anonymity in sequential releases","year":2016,"lang":"en","type":"article","venue":"Progress in Artificial Intelligence","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; k-anonymity; Naive Bayes classifier; Class (philosophy); Anonymity; Data publishing; Data mining; Identification (biology); Identifier; Publication; Data anonymization; Multi-label classification; Machine learning; Information privacy; Artificial intelligence; Publishing; 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":[0.01158505,0.0009027899,0.001943931,0.00225424,0.002782487,0.004040825,0.00218724,0.001719208,0.002004794],"category_scores_gemma":[0.04101048,0.0004211742,0.001174035,0.002454398,0.001905421,0.008899036,0.004692547,0.002705891,0.0008051958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001511841,"about_ca_system_score_gemma":0.00296507,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001930169,"about_ca_topic_score_gemma":0.003082572,"domain_scores_codex":[0.9887049,0.003733607,0.000910497,0.001866463,0.00376599,0.001018647],"domain_scores_gemma":[0.952004,0.02393007,0.004575787,0.01097261,0.007307301,0.001210265],"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":[0.004137206,0.001300111,0.03143255,0.0005250566,0.0003925893,0.0007116553,0.002256063,0.205966,0.02443373,0.06881341,0.01143169,0.6485999],"study_design_scores_gemma":[0.00005764106,0.0002662578,0.003327037,0.00004017238,0.00007267761,0.0002406305,0.0006030587,0.9125437,0.01058228,0.06912942,0.003058488,0.0000786357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1500087,0.0003439425,0.8441906,0.0009343468,0.00030798,0.0001732493,0.0005468572,0.001198336,0.002295999],"genre_scores_gemma":[0.8022759,0.0001753435,0.1926886,0.0002103317,0.0003087904,0.0001433037,0.0008309114,0.00019119,0.003175627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01158505,"threshold_uncertainty_score":0.06126839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1255416464601163,"score_gpt":0.3989244207405779,"score_spread":0.2733827742804616,"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."}}