{"id":"W2336036278","doi":"","title":"Spam Filtering by Using a Compound Method of Feature Selection","year":2012,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Spam and Phishing Detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Feature selection; AdaBoost; Artificial intelligence; The Internet; Data mining; Selection (genetic algorithm); Feature (linguistics); Set (abstract data type); Machine learning; Volume (thermodynamics); Data set; Pattern recognition (psychology); Classifier (UML); World Wide Web","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":[0.0005937523,0.00008040731,0.0002309174,0.00007377462,0.0001098899,0.00001506529,0.0001265255,0.00007925962,4.361286e-7],"category_scores_gemma":[0.00002600218,0.00006197957,0.0000340377,0.0001639786,0.00002972283,0.0003255109,0.00008043754,0.0003808078,1.481662e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002737105,"about_ca_system_score_gemma":0.00001046427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002107302,"about_ca_topic_score_gemma":9.460735e-8,"domain_scores_codex":[0.9993829,0.00003283892,0.0002177608,0.00007412743,0.0001602735,0.0001321459],"domain_scores_gemma":[0.9994146,0.000111299,0.0003373283,0.0000406126,0.000047836,0.00004829251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004863794,0.00002825731,0.003144917,0.00007953992,0.0002401513,4.464618e-7,0.00617766,0.0002590356,0.9494041,0.001650022,0.005214952,0.03375232],"study_design_scores_gemma":[0.002100949,0.0004919566,0.008670581,0.0005251038,0.0003835306,0.001985754,0.004512121,0.01241181,0.9393007,0.007181418,0.02169288,0.0007431789],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4841774,0.01051848,0.5043328,0.0003387158,0.0004320218,0.00005361671,6.802266e-7,0.00001449813,0.000131762],"genre_scores_gemma":[0.9272432,0.0006382774,0.07172002,0.00007347588,0.0003074602,6.135366e-7,5.084971e-8,0.000003713323,0.00001323426],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4430658,"threshold_uncertainty_score":0.2527453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05153405964259615,"score_gpt":0.3405235945008405,"score_spread":0.2889895348582443,"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."}}