{"id":"W3098214884","doi":"10.1016/j.eswa.2020.114280","title":"Improving DEA cross-efficiency optimization in portfolio selection","year":2020,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Portfolio; Selection (genetic algorithm); Data envelopment analysis; Portfolio optimization; Computer science; Stock (firearms); Modern portfolio theory; Mathematical optimization; Econometrics; Economics; Mathematics; Finance; Machine learning","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":[0.005523596,0.00135302,0.001513544,0.001485884,0.0004880657,0.001432779,0.0008780729,0.001140226,0.001956127],"category_scores_gemma":[0.01210601,0.0006249311,0.001006228,0.001453373,0.0004530818,0.001328551,0.001136777,0.001351102,0.000493205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007853784,"about_ca_system_score_gemma":0.0009852387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002110225,"about_ca_topic_score_gemma":0.001789148,"domain_scores_codex":[0.9981738,0.001044373,0.0001174063,0.0001638466,0.0004031773,0.00009730148],"domain_scores_gemma":[0.9958674,0.002921951,0.0002008665,0.0002968546,0.000651277,0.0000617012],"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":[0.00008191383,0.0001112473,0.0005829201,0.00007960224,0.0001491713,0.00002949232,0.00002079652,0.923339,0.001801716,0.008988996,0.0006751955,0.06414002],"study_design_scores_gemma":[0.000006027271,0.00002016136,0.000154072,0.000006611737,0.00001529275,0.000008794703,0.000002357537,0.9966661,0.0007473352,0.002072098,0.0002980439,0.000003052119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01945532,0.0007592837,0.9769874,0.000135295,0.00004717729,0.00003522373,0.00003654775,0.0001346305,0.00240917],"genre_scores_gemma":[0.5420516,0.0009224481,0.4512122,0.0002474441,0.0001401726,0.0002596436,0.0002871814,0.0002142036,0.004665124],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005523596,"threshold_uncertainty_score":0.02921188,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04463200486552345,"score_gpt":0.3516503686956607,"score_spread":0.3070183638301373,"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."}}