{"id":"W4234232346","doi":"10.1109/grc.2007.4403192","title":"Naïve Bayes Text Classifier","year":2007,"lang":"en","type":"article","venue":"2007 IEEE International Conference on Granular Computing (GRC 2007)","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Naive Bayes classifier; Bayes' theorem; Computer science; Artificial intelligence; Support vector machine; Machine learning; Classifier (UML); Detector; Bayes classifier; Bayesian probability; Bayesian programming; Pattern recognition (psychology); Bayes factor","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":[0.001894236,0.0007593278,0.001398252,0.002791155,0.001122347,0.003272021,0.001677987,0.002027469,0.01933733],"category_scores_gemma":[0.007539363,0.0004328804,0.0006759752,0.001964188,0.0008058981,0.003184685,0.0008836953,0.001099091,0.01622907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001009199,"about_ca_system_score_gemma":0.001118483,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003759299,"about_ca_topic_score_gemma":0.003903418,"domain_scores_codex":[0.9979413,0.0002730912,0.0001817907,0.000348144,0.001092907,0.0001627537],"domain_scores_gemma":[0.9976081,0.0008872498,0.0001701036,0.0003339174,0.0009291419,0.0000715155],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002770578,0.0001835404,0.005319546,0.000345774,0.00005851877,0.0002336061,0.0001378016,0.009702117,0.01155927,0.04216242,0.03603555,0.8939847],"study_design_scores_gemma":[0.00008559829,0.0001707795,0.004949506,0.0003320787,0.00008681775,0.001391658,0.0002362311,0.7077721,0.03836014,0.1055969,0.1409014,0.0001168444],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01762235,0.001917454,0.920662,0.001180331,0.0007035829,0.0004203598,0.001842724,0.008309562,0.04734172],"genre_scores_gemma":[0.2325462,0.00163512,0.6866549,0.0008612757,0.0005124361,0.0005274321,0.003141625,0.0005522208,0.07356877],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01933733,"threshold_uncertainty_score":0.06468982,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06247321536509361,"score_gpt":0.3143555518865284,"score_spread":0.2518823365214348,"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."}}