{"id":"W2139499710","doi":"","title":"Presenting a Hybrid Feature Selection Method Using Chi2 and DMNB Wrapper for E-Mail Spam Filtering","year":2013,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Feature selection; Support vector machine; Computer science; Classifier (UML); Artificial intelligence; Random forest; Pattern recognition (psychology); Word error rate; Machine learning; Feature extraction; Information gain; Filter (signal processing); Data mining","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004801601,0.0001115007,0.0002376343,0.00009895808,0.0002903758,0.00009961461,0.0001228622,0.00006777064,5.766038e-7],"category_scores_gemma":[0.00006694932,0.00008519646,0.0000409788,0.0001081872,0.00002948924,0.0004552299,0.0001249504,0.0003880817,2.121745e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001941946,"about_ca_system_score_gemma":0.00001303293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004049002,"about_ca_topic_score_gemma":2.593217e-7,"domain_scores_codex":[0.9992777,0.00002498002,0.0002271636,0.0001656101,0.0001340236,0.0001705722],"domain_scores_gemma":[0.9993732,0.0001747639,0.0002671283,0.00004646229,0.0000843929,0.00005403044],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001313632,0.00003185168,0.0041371,0.0004263686,0.0007764534,0.000003046213,0.01016894,0.001472081,0.73237,0.002798795,0.01179537,0.2358886],"study_design_scores_gemma":[0.006097963,0.0008898225,0.02496152,0.001061226,0.0006595601,0.003651346,0.007130269,0.464095,0.3710977,0.09770975,0.02090832,0.001737515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5891396,0.00268582,0.4068079,0.0008926893,0.0002289818,0.000172009,4.056719e-7,0.0000208967,0.00005163397],"genre_scores_gemma":[0.8388278,0.0006744108,0.1597721,0.000146418,0.0005209545,0.00001007373,7.17107e-8,0.00000753709,0.00004061797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4626229,"threshold_uncertainty_score":0.3474211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04238298824576507,"score_gpt":0.3269562397928041,"score_spread":0.2845732515470391,"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."}}