{"id":"W1520425575","doi":"10.1007/978-3-540-73451-2_37","title":"Rough Set Approach to Spam Filter Learning","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Heuristics; Rough set; Focus (optics); Filter (signal processing); Set (abstract data type); Construct (python library); Decision table; Probabilistic logic; Artificial intelligence; Simple (philosophy); Data mining; Machine learning","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":[0.002783179,0.0007831365,0.002670222,0.00232298,0.0006641504,0.002535214,0.002093936,0.001474298,0.001587958],"category_scores_gemma":[0.007264938,0.0006128288,0.001448049,0.001927269,0.001437097,0.002374949,0.0009947603,0.002203105,0.0004403311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001644388,"about_ca_system_score_gemma":0.0009500758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002277241,"about_ca_topic_score_gemma":0.001783482,"domain_scores_codex":[0.9977503,0.0009042455,0.0001267929,0.0002038235,0.0009099606,0.0001047712],"domain_scores_gemma":[0.9968905,0.002183661,0.0001404841,0.000242526,0.0004846883,0.00005814787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001135278,0.0001165016,0.0005742569,0.0005536768,0.0003621164,0.0001867448,0.0003095782,0.3446749,0.001923741,0.4501997,0.007894703,0.1930906],"study_design_scores_gemma":[0.00001722836,0.0000481076,0.0002260753,0.00003750631,0.00006362479,0.00009055972,0.00003538545,0.6028404,0.0008171045,0.3919225,0.003868025,0.00003351678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002736897,0.002381522,0.9925491,0.0003899196,0.00008744581,0.00002555093,0.00004741111,0.0001134998,0.001668649],"genre_scores_gemma":[0.2553417,0.005557476,0.7305688,0.0003649846,0.000800942,0.0002441743,0.0003242799,0.00008251234,0.006715138],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002783179,"threshold_uncertainty_score":0.01471901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04480794662812257,"score_gpt":0.2649052055672635,"score_spread":0.2200972589391409,"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."}}