{"id":"W2166045895","doi":"10.1007/11731139_15","title":"Boosting Prediction Accuracy on Imbalanced Datasets with SVM Ensembles","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":149,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Boosting (machine learning); Computer science; Support vector machine; Artificial intelligence; Machine learning; Data mining; Sampling (signal processing); Context (archaeology); Ensemble learning; Oversampling; Pattern recognition (psychology); Data sampling; Bandwidth (computing)","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":[0.0068789,0.001302327,0.002099989,0.001490589,0.0003855068,0.001055038,0.00125803,0.001176957,0.000983529],"category_scores_gemma":[0.01454568,0.0005235376,0.0008439003,0.001158127,0.0003539684,0.00235027,0.001444054,0.002199244,0.001041979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004261023,"about_ca_system_score_gemma":0.0004584527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001105986,"about_ca_topic_score_gemma":0.001473472,"domain_scores_codex":[0.9978362,0.0007862785,0.0001229278,0.0003441338,0.0007171502,0.0001933863],"domain_scores_gemma":[0.9911702,0.004976322,0.0003201117,0.001534261,0.001793308,0.0002059059],"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":[0.0009591361,0.0004877168,0.01432199,0.0001546918,0.0003779512,0.00009737578,0.00009083235,0.3001433,0.007079797,0.002450825,0.01544561,0.6583908],"study_design_scores_gemma":[0.00001443568,0.00008399004,0.001357042,0.00001162746,0.00003902199,0.00003108897,0.000009164651,0.99361,0.002081124,0.002349468,0.0004059249,0.00000709808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3218287,0.003910728,0.6611638,0.001013833,0.001152109,0.0001577795,0.0005406148,0.003431909,0.0068005],"genre_scores_gemma":[0.8847052,0.0006056139,0.1102261,0.0002250179,0.000629677,0.00008557748,0.0009395985,0.0001956541,0.002387657],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0068789,"threshold_uncertainty_score":0.03637952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0174732329986504,"score_gpt":0.253741026703638,"score_spread":0.2362677937049876,"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."}}