{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005792141,0.0008876752,0.0006514982,0.001245306,0.0004795728,0.0009508124,0.0005989362,0.0007399194,0.0006450452],"category_scores_gemma":[0.01489643,0.0002865365,0.0005641209,0.0006468105,0.0006284176,0.0009412025,0.0006933612,0.0009209439,0.0001276484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018365,"about_ca_system_score_gemma":0.001108747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002948538,"about_ca_topic_score_gemma":0.001886986,"domain_scores_codex":[0.998743,0.0006603431,0.00005906812,0.0001136495,0.0003373893,0.00008651613],"domain_scores_gemma":[0.9941344,0.004144688,0.0003406064,0.0002503704,0.001037801,0.00009220871],"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":[0.0001486832,0.0001509172,0.004070435,0.00006742468,0.00006370626,0.00004697916,0.0001166092,0.8929443,0.004763204,0.005504906,0.000287063,0.09183574],"study_design_scores_gemma":[0.000004332659,0.00005256754,0.0007105402,0.000005707799,0.000005850281,0.00001206944,0.00001411021,0.9966487,0.001420605,0.00100818,0.0001121648,0.000005124495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1711705,0.000210992,0.8259957,0.0003163752,0.00002099331,0.00011852,0.00004579058,0.0002307592,0.001890312],"genre_scores_gemma":[0.7729133,0.000113575,0.2257251,0.00005985587,0.00001471013,0.00009706501,0.0001081335,0.00006683343,0.0009013927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005792141,"threshold_uncertainty_score":0.03063214,"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."}}