{"id":"W4378419870","doi":"10.1007/978-3-031-34020-8_20","title":"Binary Black Widow with Hill Climbing Algorithm for Feature Selection","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Hill climbing; Computer science; Algorithm; Feature selection; Metaheuristic; Binary number; Convergence (economics); Set (abstract data type); Ideal (ethics); Extension (predicate logic); Domain (mathematical analysis); Artificial intelligence; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00221036,0.0002660863,0.0003060357,0.001659848,0.0007193055,0.001034044,0.003030356,0.0001770793,0.000004450716],"category_scores_gemma":[0.0001080571,0.0002471123,0.00005103292,0.001291192,0.0008549691,0.00535683,0.001810071,0.0005983567,0.00006676879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000199318,"about_ca_system_score_gemma":0.0005479704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006459004,"about_ca_topic_score_gemma":0.000006647776,"domain_scores_codex":[0.9977828,0.00005832971,0.0005989774,0.0004464148,0.0007470705,0.0003664329],"domain_scores_gemma":[0.9960912,0.0005429978,0.0003815724,0.001635629,0.001191665,0.0001569297],"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":[0.000004020851,0.00002145815,0.0000105121,0.00005264228,0.00001665175,8.116339e-7,0.0009061482,0.004743462,0.000001788897,0.2821173,0.001752241,0.7103729],"study_design_scores_gemma":[0.0003701519,0.0001422882,0.0001831323,0.0001394385,0.000006174811,0.00001950851,0.0000137393,0.9559081,0.000006458035,0.002595874,0.040333,0.0002821654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001835491,0.00006357111,0.9689488,0.001584155,0.0002352975,0.0008696741,0.00003851072,0.0002055774,0.02805261],"genre_scores_gemma":[0.0001743233,0.001452995,0.9893323,0.0004739092,0.00006033116,0.00008396732,0.0001906071,0.00002297904,0.008208645],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9511646,"threshold_uncertainty_score":0.9999981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04667246751670973,"score_gpt":0.3125757170518372,"score_spread":0.2659032495351275,"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."}}