{"id":"W4386198763","doi":"10.1007/978-981-99-3734-9_12","title":"Handling Class Imbalance Problem Using Support Vector Machine","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Outlier; Class (philosophy); Computer science; Machine learning; Artificial intelligence; Hazard; Test data; Data mining","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":[0.002451458,0.001205937,0.001588565,0.001993197,0.0007871924,0.001913826,0.00232005,0.001200393,0.003118847],"category_scores_gemma":[0.008537834,0.0004767946,0.0008574128,0.002234901,0.0004266265,0.00335714,0.002004847,0.002583803,0.001626302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004062541,"about_ca_system_score_gemma":0.0007719594,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001477728,"about_ca_topic_score_gemma":0.001189863,"domain_scores_codex":[0.9980443,0.0003354278,0.0001556576,0.0003867649,0.0008887372,0.0001891799],"domain_scores_gemma":[0.9961424,0.00172519,0.00037983,0.0006039019,0.001005711,0.0001428612],"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":[0.0002490734,0.000244105,0.003547508,0.0001665719,0.0001145134,0.0002517238,0.0001286531,0.0434365,0.007530467,0.004138852,0.01808307,0.922109],"study_design_scores_gemma":[0.00001899378,0.00006639694,0.000975003,0.00002589034,0.00003976011,0.0001811618,0.0001002006,0.970595,0.006098385,0.0168349,0.005046952,0.00001732871],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02212607,0.0009483345,0.9710789,0.0003956011,0.0003554288,0.00007957743,0.0002474374,0.002995069,0.001773621],"genre_scores_gemma":[0.4170532,0.001064844,0.5698712,0.0004258345,0.0008045285,0.000235134,0.00278928,0.0006877321,0.007068261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003118847,"threshold_uncertainty_score":0.01296473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03058655104080431,"score_gpt":0.255297312766796,"score_spread":0.2247107617259917,"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."}}