{"id":"W2158191474","doi":"10.5539/cis.v6n3p48","title":"Combination of Naïve Bayes Classifier and K-Nearest Neighbor (cNK) in the Classification Based Predictive Models","year":2013,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Naive Bayes classifier; Classifier (UML); k-nearest neighbors algorithm; Bayes classifier; Artificial intelligence; Profitability index; Machine learning; Bayes' theorem; Pattern recognition (psychology); Data mining; Support vector machine; Bayesian probability; Finance","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":[0.006463998,0.001356332,0.002813093,0.003668308,0.001262913,0.002250866,0.002259708,0.001777943,0.001567038],"category_scores_gemma":[0.01416301,0.0005903254,0.001317499,0.003162506,0.0007703141,0.004540507,0.0007668202,0.001703888,0.001210737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001000315,"about_ca_system_score_gemma":0.00172758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00927368,"about_ca_topic_score_gemma":0.009778924,"domain_scores_codex":[0.9923599,0.002504708,0.0006684224,0.001551462,0.002636611,0.0002788543],"domain_scores_gemma":[0.9928418,0.004020773,0.0003057555,0.000437105,0.002272014,0.0001225982],"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":[0.000406429,0.0005613189,0.01684166,0.0006292316,0.0006246918,0.0002360597,0.0001892477,0.07133596,0.002095207,0.008569892,0.005956746,0.8925534],"study_design_scores_gemma":[0.00006019475,0.0002732073,0.00344362,0.000174653,0.000368823,0.0004956562,0.0001298104,0.9699884,0.00326941,0.01614947,0.005535203,0.0001116806],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02715645,0.006863693,0.9582475,0.0007534676,0.0006940634,0.0003691513,0.0003060469,0.001016576,0.004593126],"genre_scores_gemma":[0.4724009,0.003547667,0.5186087,0.0005042496,0.0007738843,0.0003347904,0.0007835198,0.00009388488,0.002952353],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00927368,"threshold_uncertainty_score":0.03418535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02726052324759835,"score_gpt":0.2391704484447618,"score_spread":0.2119099251971634,"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."}}