{"id":"W1409627487","doi":"","title":"Detection and Filtering Spam using Feature Selection and Learning Machine Methods","year":2015,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Feature selection; Variety (cybernetics); Decision tree; Machine learning; Data mining; Popularity; Selection (genetic algorithm); Artificial intelligence; Offensive; Tree (set theory); Feature extraction; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002502957,0.0009875976,0.001983362,0.006480068,0.0008240937,0.001342091,0.0009480394,0.00113644,0.0009185987],"category_scores_gemma":[0.004638164,0.0002852448,0.001536203,0.003676138,0.0004186083,0.001171783,0.0006217347,0.0006889763,0.0007199562],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000437019,"about_ca_system_score_gemma":0.0007190531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001686173,"about_ca_topic_score_gemma":0.000902715,"domain_scores_codex":[0.9977182,0.0006157344,0.0002487205,0.0003669328,0.0008576814,0.0001927507],"domain_scores_gemma":[0.9976682,0.001159947,0.0002917595,0.0001729762,0.0006429268,0.00006419793],"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":[0.0002793886,0.0006892345,0.01940073,0.0002479917,0.0002689061,0.0003631051,0.0001666353,0.05070347,0.01629597,0.002360591,0.004402269,0.9048218],"study_design_scores_gemma":[0.00003770765,0.0002317956,0.009078158,0.0000392724,0.0001356214,0.0004057369,0.000122469,0.9712322,0.01164948,0.004178954,0.002845368,0.0000433099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1009821,0.001416173,0.8928233,0.0004447852,0.0001561428,0.0002466285,0.000248488,0.002142732,0.001539598],"genre_scores_gemma":[0.6255701,0.0008095886,0.3701062,0.000219357,0.0003313083,0.0003163477,0.0008154455,0.00007386511,0.001757828],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006480068,"threshold_uncertainty_score":0.01323712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06891504302138124,"score_gpt":0.3569574012045877,"score_spread":0.2880423581832064,"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."}}