{"id":"W4387882788","doi":"10.1109/mlsp55844.2023.10285890","title":"Robust Feature Selection With Weight Cost Maximin Optimization","year":2023,"lang":"en","type":"article","venue":"","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Minimax; Feature selection; Mathematical optimization; Computer science; Minimum redundancy feature selection; Pattern recognition (psychology); Feature (linguistics); Regularization (linguistics); Feature extraction; Optimization problem; Selection (genetic algorithm); Artificial intelligence; Mathematics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001069962,0.00008117683,0.0000848931,0.000114175,0.0001677474,0.0001742418,0.0001937059,0.00003957818,0.0001164036],"category_scores_gemma":[0.00000721839,0.00005742933,0.00002930377,0.001723057,0.00001144479,0.0004013655,0.00006644355,0.00006964614,0.0000609654],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002232687,"about_ca_system_score_gemma":0.00002028668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002117086,"about_ca_topic_score_gemma":0.00005834847,"domain_scores_codex":[0.9993209,0.00001537356,0.00007918078,0.0002406879,0.0001607357,0.0001831595],"domain_scores_gemma":[0.9996504,0.00001549554,0.00004659059,0.0001613861,0.00008097891,0.00004512208],"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":[0.000004669251,0.000009675558,0.0007787866,0.00000377289,0.00002011762,0.000005765335,0.0000902223,0.9626014,0.0000285658,0.004642675,0.009159439,0.02265486],"study_design_scores_gemma":[0.00009951762,0.00004224131,0.0007372875,0.000006323657,0.000006963174,0.00001448118,0.00003017564,0.9938383,0.0002275408,0.00002680181,0.004875188,0.00009518863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005846066,0.000007603842,0.9899039,0.001561648,0.00006123599,0.00007663986,4.601638e-7,0.0004115626,0.00739237],"genre_scores_gemma":[0.07725456,0.00003709616,0.8938308,0.0002634401,0.0001920091,0.00002635285,0.00004977823,0.00002398113,0.02832198],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09607308,"threshold_uncertainty_score":0.23419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0197943166458435,"score_gpt":0.1997420549839656,"score_spread":0.1799477383381221,"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."}}