{"id":"W3013687893","doi":"10.5383/juspn.08.02.001","title":"Bankruptcy prediction and risk scoring using Hybrid Discriminant Neural Networks","year":2017,"lang":"en","type":"article","venue":"Journal of Ubiquitous Systems and Pervasive Networks","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Linear discriminant analysis; Artificial neural network; Bankruptcy; Discriminant; Portfolio; Reliability (semiconductor); Multiple discriminant analysis; Computer science; Artificial intelligence; Machine learning; Bankruptcy prediction; Econometrics; Data mining; Actuarial science; Business; Finance; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00129048,0.0005616187,0.0005159019,0.001140706,0.0002315453,0.0007192594,0.0005626027,0.0004583895,0.0006420032],"category_scores_gemma":[0.002097286,0.0002178379,0.0004758811,0.0006638527,0.0002432178,0.0005700489,0.0006430672,0.0004707898,0.0001350819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007044428,"about_ca_system_score_gemma":0.0003890935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00934434,"about_ca_topic_score_gemma":0.00621148,"domain_scores_codex":[0.9996686,0.0001444942,0.00001990065,0.00006809492,0.00005304426,0.00004590033],"domain_scores_gemma":[0.9991853,0.0004570815,0.00008585842,0.00004369323,0.000187818,0.00004018711],"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":[0.0004543252,0.0002705843,0.03111118,0.00004844934,0.000162146,0.00013053,0.00009551179,0.8326065,0.002747025,0.001849343,0.0007413162,0.129783],"study_design_scores_gemma":[0.000002206745,0.00001542282,0.001228021,0.00000176275,0.000004925091,0.000005249146,0.000006884544,0.9982552,0.0001701532,0.0002748713,0.00003219683,0.000003029825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7254061,0.00046621,0.2713344,0.0002197877,0.00005546979,0.00004903832,0.0001980372,0.0003208511,0.001950168],"genre_scores_gemma":[0.9852874,0.00005588341,0.01388178,0.0000127142,0.00001208074,0.00001753404,0.00007071855,0.000005621655,0.0006562768],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00934434,"threshold_uncertainty_score":0.0185799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02281904233818544,"score_gpt":0.2311638341336785,"score_spread":0.2083447917954931,"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."}}