{"id":"W2043316303","doi":"10.1109/cjece.2006.259180","title":"Comparing nearest-neighbour search strategies in the SMOTE algorithm","year":2006,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; k-nearest neighbors algorithm; Nearest neighbour; Algorithm; Nearest-neighbor chain algorithm; Nearest neighbor search; Artificial intelligence; Pattern recognition (psychology); Cluster analysis","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003251175,0.0001040361,0.000151852,0.0003520029,0.00007405356,0.0007426144,0.0007348685,0.00002728513,0.000001321657],"category_scores_gemma":[0.000005292843,0.00007884067,0.00003806621,0.0005410609,0.00001771705,0.000528868,0.0000439347,0.0003146747,0.000001322793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004336549,"about_ca_system_score_gemma":0.0001383992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001972189,"about_ca_topic_score_gemma":0.0005380925,"domain_scores_codex":[0.999083,0.00003180146,0.0002267867,0.0001257264,0.0001709359,0.0003616908],"domain_scores_gemma":[0.9995682,0.00008401326,0.00003565742,0.0001274939,0.00003852371,0.0001461023],"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":[0.00000401628,0.00008062661,0.01203218,0.00005216199,0.00007655284,0.003346217,0.0009809051,0.1601844,0.00004532577,0.2903257,0.00348072,0.5293912],"study_design_scores_gemma":[0.000201318,0.00007878349,0.06146151,0.00002272406,0.000003241134,0.0001720305,0.00001086723,0.9338335,0.00001045652,0.0008714408,0.003228227,0.0001059271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04328427,0.0006424443,0.9551992,0.0004200602,0.0001829212,0.00005157346,6.244318e-7,0.000012341,0.0002065676],"genre_scores_gemma":[0.9592718,0.00001151635,0.04025387,0.00009365906,0.0003509104,7.587435e-7,0.00000115236,0.000005376258,0.00001096866],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9159876,"threshold_uncertainty_score":0.7161047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009873257376333613,"score_gpt":0.190715001329723,"score_spread":0.1808417439533894,"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."}}