{"id":"W2126734246","doi":"10.1007/s10115-009-0198-y","title":"Boosting support vector machines for imbalanced data sets","year":2009,"lang":"en","type":"article","venue":"Knowledge and Information Systems","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":279,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"National Institutes of Health","keywords":"Support vector machine; Boosting (machine learning); Classifier (UML); Machine learning; Artificial intelligence; Computer science; Margin classifier; Data mining; Structured support vector machine; Relevance vector machine; Pattern recognition (psychology)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005966123,0.0001187576,0.0001665566,0.00012892,0.0001536226,0.0004052713,0.0008417442,0.00006325058,0.000001474214],"category_scores_gemma":[0.0001389793,0.0001043766,0.00001824412,0.0002018749,0.00001457913,0.006741441,0.0001657871,0.00005913844,0.00006596997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002859385,"about_ca_system_score_gemma":0.00006388925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004030068,"about_ca_topic_score_gemma":8.231892e-7,"domain_scores_codex":[0.9990044,0.00002704536,0.0004564001,0.0001999501,0.0001263576,0.0001858298],"domain_scores_gemma":[0.9985817,0.00006994687,0.000227254,0.0008392452,0.0002161107,0.00006570998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001407183,0.00004768093,0.0004486191,0.0003374548,0.00001696981,4.414367e-7,0.002476966,0.000004635871,0.001087167,0.3098598,0.1801419,0.5055643],"study_design_scores_gemma":[0.0004115945,0.0001109259,0.004568014,0.00005826338,0.000004503831,0.0000294131,0.00004517331,0.3933702,0.0006587317,0.0003552405,0.6001729,0.000215015],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003040078,0.0002586794,0.9745212,0.0005168449,0.0005639926,0.0007288966,0.0002305308,0.0005936896,0.02228214],"genre_scores_gemma":[0.968823,0.00005544183,0.02850256,0.0005063423,0.0001770562,0.00009187179,0.001496064,0.000006733002,0.0003409789],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.968519,"threshold_uncertainty_score":0.4887382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03635550629993588,"score_gpt":0.3092264469905259,"score_spread":0.27287094069059,"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."}}