{"id":"W2241877823","doi":"10.1007/s11227-015-1541-6","title":"Improving the classification performance of biological imbalanced datasets by swarm optimization algorithms","year":2015,"lang":"en","type":"article","venue":"The Journal of Supercomputing","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University","funders":"Universidade de Macau","keywords":"Computer science; Machine learning; Artificial intelligence; Class (philosophy); Data mining; Swarm behaviour; Particle swarm optimization; Population; Algorithm","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":[0.002831497,0.0008883901,0.001054495,0.001290171,0.0005952746,0.001543703,0.0008993269,0.0009284026,0.0007944601],"category_scores_gemma":[0.006372492,0.0003059154,0.0006145378,0.001060384,0.0005323348,0.00181585,0.0009685677,0.001081283,0.0004737096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007194696,"about_ca_system_score_gemma":0.0006444625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002495809,"about_ca_topic_score_gemma":0.001664699,"domain_scores_codex":[0.9992765,0.000233258,0.00006202275,0.0001380031,0.0002128409,0.00007739074],"domain_scores_gemma":[0.9975957,0.00123405,0.000209067,0.0003647456,0.0005013227,0.00009500024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006659612,0.0005048616,0.01358331,0.0001541081,0.0002355395,0.00008566126,0.0002382475,0.5873432,0.01401512,0.006205947,0.007439774,0.3695283],"study_design_scores_gemma":[0.00001248377,0.00003435369,0.0006559441,0.0000031806,0.000007844205,0.000007393396,0.00001632063,0.996222,0.001247098,0.001516603,0.0002735306,0.000003210208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4368338,0.001340035,0.5530288,0.001404305,0.0004342352,0.0001224488,0.0002648959,0.001716219,0.004855289],"genre_scores_gemma":[0.8482431,0.0003406292,0.1484064,0.0002488746,0.0001830999,0.0001112088,0.0006275367,0.0001418007,0.001697436],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002831497,"threshold_uncertainty_score":0.01497459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05449515389303571,"score_gpt":0.2758229984584579,"score_spread":0.2213278445654222,"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."}}