{"id":"W2017733151","doi":"10.1109/iccit.2007.148","title":"Investigating the Performance of Naive- Bayes Classifiers and K- Nearest Neighbor Classifiers","year":2007,"lang":"en","type":"article","venue":"2007 International Conference on Convergence Information Technology (ICCIT 2007)","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":151,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Naive Bayes classifier; Artificial intelligence; k-nearest neighbors algorithm; Computer science; Machine learning; Bayes error rate; Classifier (UML); Bayes classifier; Random subspace method; Pattern recognition (psychology); Bayesian probability; Bayes' theorem; Data mining; Support vector machine","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":[0.02497856,0.001716046,0.00247018,0.004359988,0.001894285,0.003885977,0.001954323,0.002871073,0.001898506],"category_scores_gemma":[0.1016264,0.0006303834,0.001129974,0.003232703,0.001251755,0.007553869,0.001059398,0.001610738,0.001182721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002820689,"about_ca_system_score_gemma":0.002246718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01523003,"about_ca_topic_score_gemma":0.01199921,"domain_scores_codex":[0.972265,0.009156005,0.002022311,0.003400401,0.01221915,0.0009371641],"domain_scores_gemma":[0.9158292,0.06108084,0.002818109,0.003646336,0.01587648,0.0007489646],"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":[0.002737798,0.0007120625,0.03973011,0.001219689,0.0009415638,0.0002295474,0.00084799,0.2404572,0.002506417,0.02304417,0.01211093,0.6754625],"study_design_scores_gemma":[0.00007788355,0.0006781075,0.0085443,0.0001624138,0.0001744215,0.0002316601,0.0004847309,0.9636276,0.003033429,0.01852527,0.004346051,0.0001140309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4079919,0.02904348,0.520718,0.003364038,0.002024973,0.001035505,0.001720448,0.002447348,0.03165425],"genre_scores_gemma":[0.7794862,0.003160249,0.2112149,0.0004192465,0.0004650626,0.000240488,0.001432079,0.0002089083,0.003372857],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02497856,"threshold_uncertainty_score":0.1321008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02490013171729916,"score_gpt":0.2604650408833423,"score_spread":0.2355649091660431,"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."}}