{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002577574,0.001517604,0.001905045,0.001051269,0.0004502021,0.000939853,0.001024504,0.001080344,0.001211321],"category_scores_gemma":[0.006146414,0.0005991468,0.00112221,0.001294046,0.0007990086,0.001013104,0.0009271062,0.001151386,0.0005773754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006864491,"about_ca_system_score_gemma":0.0008226254,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001349371,"about_ca_topic_score_gemma":0.001081579,"domain_scores_codex":[0.9985102,0.0006394691,0.00009513757,0.0002723452,0.0003730476,0.0001097902],"domain_scores_gemma":[0.9982431,0.001102127,0.0001505968,0.000171989,0.0002925223,0.00003966255],"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":[0.0003114271,0.0001568916,0.001025247,0.0001264194,0.0001734017,0.0001633213,0.00006717551,0.73129,0.01448707,0.009181997,0.003449336,0.2395677],"study_design_scores_gemma":[0.00001330953,0.00002701912,0.0001798148,0.000002692514,0.00000562263,0.00001835604,0.000002862457,0.9953056,0.001367357,0.002795615,0.0002753746,0.000006401063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005864077,0.00009810043,0.9934869,0.00006778842,0.00001260146,0.00002982706,0.0000262076,0.0002450125,0.0001695568],"genre_scores_gemma":[0.3742528,0.0001931106,0.621967,0.0001833919,0.0001165472,0.0004574998,0.0004840172,0.0002570481,0.002088648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002577574,"threshold_uncertainty_score":0.0136317,"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."}}