{"id":"W2126915984","doi":"10.1109/cec.2010.5586163","title":"An analysis of clustering objectives for feature selection applied to encrypted traffic identification","year":2010,"lang":"en","type":"article","venue":"","topic":"Internet Traffic Analysis and Secure E-voting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"National Institute for Materials Science; Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Cluster analysis; Computer science; False positive paradox; Identification (biology); Data mining; Genetic algorithm; Feature selection; Selection (genetic algorithm); Transformation (genetics); Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Machine learning","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":[],"consensus_categories":[],"category_scores_codex":[0.0004409325,0.000111038,0.0002507717,0.0005410184,0.0001014046,0.0001553862,0.0004844085,0.00008656191,0.00001822341],"category_scores_gemma":[0.00002951947,0.00009920951,0.0001646015,0.001507029,0.00001365055,0.0002324557,0.00003324389,0.0001110887,0.000003130986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002126235,"about_ca_system_score_gemma":0.00002028749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001437798,"about_ca_topic_score_gemma":0.00395343,"domain_scores_codex":[0.9989047,0.00002359943,0.0002798807,0.0004394946,0.0001799974,0.0001723295],"domain_scores_gemma":[0.999269,0.00005450965,0.0001394355,0.000240459,0.0002269661,0.00006963039],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002536347,0.000148322,0.0001324504,0.00001217697,0.0005111779,1.412264e-7,0.004650909,0.6191363,0.2139022,0.1326994,0.00009502238,0.02868645],"study_design_scores_gemma":[0.00008633106,0.00005393415,0.0075535,0.000001548056,0.0001665816,5.510364e-7,0.0001871019,0.9823986,0.009388164,0.000006167543,0.00004096634,0.0001165385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3793121,0.000001060039,0.6202324,0.00004758966,0.00009278949,0.0001350431,0.000001702337,0.0000832052,0.00009415106],"genre_scores_gemma":[0.944739,1.935363e-7,0.05498744,0.00005268107,0.00005621808,0.00002508644,0.00002554685,0.000006370352,0.0001075055],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5654269,"threshold_uncertainty_score":0.4045646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007318214795763238,"score_gpt":0.2562283309916138,"score_spread":0.2489101161958505,"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."}}