{"id":"W4389671032","doi":"10.1016/j.asoc.2023.111141","title":"Improved binary differential evolution with dimensionality reduction mechanism and binary stochastic search for feature selection","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Dimensionality reduction; Feature selection; Binary number; Support vector machine; Classifier (UML); Curse of dimensionality; Binary classification; Artificial intelligence; Pattern recognition (psychology); Local optimum; Differential evolution; Feature vector; Machine learning; Data mining; Algorithm; Mathematics","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":[0.0007808628,0.0002038178,0.0002299604,0.0003389463,0.0008227088,0.0001866851,0.000278229,0.0001176823,0.00000319252],"category_scores_gemma":[0.00004835553,0.0001870952,0.00003791056,0.001227437,0.00007233238,0.000174056,0.000425071,0.0003077676,0.000008048832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000123335,"about_ca_system_score_gemma":0.0001456342,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000182452,"about_ca_topic_score_gemma":7.544659e-7,"domain_scores_codex":[0.9979262,0.00009931893,0.000229145,0.0007472762,0.0004992171,0.000498782],"domain_scores_gemma":[0.9988846,0.0003090938,0.000109695,0.0002764806,0.0002812507,0.0001389013],"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":[0.0004599802,0.0002921346,0.00006704585,0.0003381883,0.0002059585,0.00000937331,0.001344502,0.4449526,0.287732,0.2233133,0.000846233,0.04043865],"study_design_scores_gemma":[0.0007879114,0.000214955,0.001548754,0.00002104213,0.00001471972,0.00002815091,0.00008523877,0.9926851,0.001493858,0.002909011,0.00000370987,0.0002075134],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1404445,0.00001300695,0.85763,0.00033964,0.0002479437,0.0008302833,0.000004751028,0.0004771455,0.00001276121],"genre_scores_gemma":[0.8469257,0.000001520961,0.1526538,0.00001370937,0.0001511466,0.00005050475,0.00006102542,0.00002521611,0.0001173004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7064813,"threshold_uncertainty_score":0.7629523,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751821104813373,"score_gpt":0.2681505941838864,"score_spread":0.2506323831357526,"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."}}