{"id":"W2802761309","doi":"10.1016/j.aci.2018.04.001","title":"Hybrid binary bat enhanced particle swarm optimization algorithm for solving feature selection problems","year":2018,"lang":"en","type":"article","venue":"Applied Computing and Informatics","topic":"Metaheuristic Optimization Algorithms Research","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Thompson Rivers University","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Particle swarm optimization; Computer science; Multi-swarm optimization; Feature (linguistics); Algorithm; Feature selection; Binary number; Set (abstract data type); Binary search algorithm; Metaheuristic; Swarm behaviour; Hybrid algorithm (constraint satisfaction); Bat algorithm; Mathematical optimization; Meta-optimization; Selection (genetic algorithm); Feature vector; Search algorithm; Artificial intelligence; Mathematics; Constraint satisfaction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009006101,0.0007925085,0.001156642,0.0007429215,0.00043698,0.000822297,0.001112346,0.001054845,0.00155926],"category_scores_gemma":[0.001785839,0.0003869119,0.0006294061,0.0009797669,0.0004238409,0.0007949008,0.0007142792,0.0009762564,0.0005438218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003280337,"about_ca_system_score_gemma":0.0007532724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003538234,"about_ca_topic_score_gemma":0.002388276,"domain_scores_codex":[0.9993683,0.0001912541,0.00004191275,0.00008693962,0.0002610641,0.00005064517],"domain_scores_gemma":[0.9993904,0.0002862177,0.00005619061,0.00004711075,0.0001933572,0.00002670616],"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":[0.0001714594,0.0001309578,0.001787377,0.0002063017,0.0001568138,0.0001099271,0.0001012538,0.7651731,0.008025309,0.009385897,0.003231858,0.2115198],"study_design_scores_gemma":[0.00002166161,0.00003207285,0.000216778,0.000005454077,0.000008692415,0.00002821579,0.000006920654,0.9968705,0.0006514309,0.0011892,0.0009637468,0.0000054272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01066385,0.0004284235,0.986226,0.0001362445,0.00007362621,0.00005346229,0.00003234344,0.0002612731,0.002124771],"genre_scores_gemma":[0.3399542,0.0005218489,0.653441,0.0002850641,0.000107996,0.0004239919,0.0002854631,0.0001087289,0.004871733],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003538234,"threshold_uncertainty_score":0.007035255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267380492240101,"score_gpt":0.2571261389554496,"score_spread":0.2444523340330486,"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."}}