{"id":"W2566504438","doi":"10.1109/camad.2016.7790325","title":"Efficient algorithm selection for packet classification using machine learning","year":2016,"lang":"en","type":"article","venue":"","topic":"Network Packet Processing and Optimization","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Machine learning; Artificial intelligence; Selection (genetic algorithm); Meta learning (computer science); Artificial neural network; Statistical classification; Algorithm; Network packet; Process (computing); Weighted Majority Algorithm; Feature selection; Selection algorithm; Wake-sleep algorithm; Data mining; Generalization error; Engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.002861266,0.001007132,0.001563128,0.002138023,0.0006037341,0.001500213,0.001526447,0.0009600871,0.001127513],"category_scores_gemma":[0.007319285,0.0004715758,0.0009323938,0.001581343,0.0005497804,0.001470146,0.0007891934,0.001341957,0.0005477481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009041779,"about_ca_system_score_gemma":0.001279463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001086049,"about_ca_topic_score_gemma":0.00110168,"domain_scores_codex":[0.9977242,0.0008702466,0.0001980876,0.0002976745,0.0007275864,0.0001822076],"domain_scores_gemma":[0.9964606,0.002093904,0.0003018258,0.0004537251,0.0006200729,0.00006990611],"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":[0.0002532794,0.0002554359,0.003573916,0.0001428468,0.0001915348,0.0001221993,0.00006817877,0.3523192,0.008586043,0.007668342,0.003061416,0.6237575],"study_design_scores_gemma":[0.00001403035,0.00003641905,0.0001937108,0.000007327717,0.00001405428,0.00003090968,0.000008743839,0.9920026,0.003068105,0.004010532,0.0006072158,0.000006373302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02765013,0.0005688679,0.9689662,0.0001886038,0.00005306099,0.0001053605,0.00004882584,0.001214462,0.001204369],"genre_scores_gemma":[0.3823746,0.0003160537,0.6153426,0.0001374816,0.00007067091,0.0002507278,0.0003414114,0.0001732474,0.0009932043],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002861266,"threshold_uncertainty_score":0.01513195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02901309135047316,"score_gpt":0.2669243546296606,"score_spread":0.2379112632791875,"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."}}