{"id":"W2604587950","doi":"10.1016/j.comnet.2017.04.021","title":"Extensions to decision-tree based packet classification algorithms to address new classification paradigms","year":2017,"lang":"en","type":"article","venue":"Computer Networks","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Decision tree; Algorithm; Decision tree learning; Network packet; Statistical classification; Tree (set theory); ID3 algorithm; Artificial intelligence; Machine learning; Incremental decision tree; Data mining; Computer network","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":[0.003013178,0.00086816,0.001051261,0.001150317,0.0005846621,0.001853688,0.002387289,0.00106556,0.004448977],"category_scores_gemma":[0.009779608,0.000362903,0.000892935,0.001816707,0.0003841075,0.003098028,0.001343645,0.002451737,0.002571209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006039934,"about_ca_system_score_gemma":0.001404608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00220216,"about_ca_topic_score_gemma":0.003490284,"domain_scores_codex":[0.9985581,0.0004055267,0.0001512439,0.00020402,0.0005820325,0.00009910941],"domain_scores_gemma":[0.9948826,0.001782158,0.0002601415,0.0009925563,0.001886034,0.0001965469],"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":[0.0002512908,0.0007830497,0.003982066,0.0002256076,0.0002526425,0.0001098935,0.0001388841,0.1668367,0.005237375,0.05298221,0.0178449,0.7513555],"study_design_scores_gemma":[0.00001930814,0.00006086152,0.0003005556,0.00002265431,0.00004558986,0.00006927654,0.00001421619,0.9620243,0.001528001,0.03037829,0.005521155,0.00001592508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007435294,0.0004858118,0.9886383,0.0003359352,0.0002836814,0.00009508347,0.0001598784,0.0007226292,0.001843288],"genre_scores_gemma":[0.1878535,0.001353608,0.8017121,0.0006157849,0.0006882036,0.0002951611,0.001112747,0.0002399064,0.006129006],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004448977,"threshold_uncertainty_score":0.01593542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05632261106630987,"score_gpt":0.3100486628883545,"score_spread":0.2537260518220446,"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."}}