{"id":"W4403786036","doi":"10.2139/ssrn.5000604","title":"Large-Scale Multi-Objective Feature Selection: A Multi-Phase Search Space Shrinking Approach","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University; University of Ontario Institute of Technology","funders":"","keywords":"Scale (ratio); Feature selection; Selection (genetic algorithm); Feature (linguistics); Resizing; Space (punctuation); Computer science; Phase (matter); Scale space; Pattern recognition (psychology); Artificial intelligence; Mathematical optimization; Algorithm; Mathematics; Data mining; Physics; Geography; Cartography; Economics","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","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004538423,0.0005871129,0.0005141037,0.00065016,0.0007453403,0.001313839,0.002026051,0.0005819686,0.00000926506],"category_scores_gemma":[0.0001126097,0.0005320905,0.0003816135,0.000939036,0.0000562424,0.000357917,0.00194918,0.02114383,0.0001070532],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002267006,"about_ca_system_score_gemma":0.005700327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001326993,"about_ca_topic_score_gemma":0.0007257674,"domain_scores_codex":[0.993481,0.0006665479,0.0004557353,0.001504182,0.0008193324,0.003073236],"domain_scores_gemma":[0.9980469,0.00006754586,0.0003756033,0.0009255828,0.0003427224,0.0002416681],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003634853,0.005938219,0.003269148,0.001260449,0.003331693,0.0001427352,0.04257376,0.02574075,0.001890034,0.6955885,0.003287095,0.2166141],"study_design_scores_gemma":[0.001912567,0.0002876622,0.0004523093,0.0001948939,0.0001026442,0.001531547,0.002404407,0.958244,0.0001004424,0.03072988,0.003315702,0.0007239321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004902862,0.007431325,0.9820301,0.002869762,0.0008939165,0.0004918624,0.00002597635,0.0005105907,0.0008436057],"genre_scores_gemma":[0.736962,0.002285351,0.24245,0.0001310965,0.001778708,0.0001115361,0.0002258955,0.000155646,0.01589982],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9325033,"threshold_uncertainty_score":0.9999364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02129647725193712,"score_gpt":0.316415797269649,"score_spread":0.2951193200177119,"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."}}