{"id":"W34303698","doi":"10.1007/978-3-642-23713-3_22","title":"Granular Data Regression with Neural Networks","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Granularity; Computer science; Granular computing; Interval (graph theory); Data mining; Interval data; Artificial neural network; Artificial intelligence; Multilayer perceptron; Perceptron; Pattern recognition (psychology); Algorithm; Machine learning; Rough set; Mathematics","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":[0.002216736,0.0009362985,0.001760702,0.00113249,0.0002976728,0.001648754,0.001115353,0.0009645393,0.00326887],"category_scores_gemma":[0.006798095,0.000704636,0.0009638747,0.002436407,0.000616596,0.002079401,0.001599774,0.001850449,0.001079395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005274721,"about_ca_system_score_gemma":0.0003123584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001578478,"about_ca_topic_score_gemma":0.001368413,"domain_scores_codex":[0.9990938,0.0003153025,0.00007450252,0.000193033,0.0002649283,0.00005847737],"domain_scores_gemma":[0.9983464,0.001053585,0.0001355827,0.0002749352,0.0001604094,0.00002908217],"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":[0.0001816184,0.00006201676,0.0006633046,0.0002322684,0.0002012492,0.000086321,0.00004220867,0.6392208,0.00211626,0.03226068,0.00389132,0.321042],"study_design_scores_gemma":[0.000004219226,0.000007115833,0.00009654611,0.00001154425,0.000009802774,0.00001120698,0.000003709199,0.9811348,0.0003832398,0.01772293,0.000609963,0.000004947181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004297326,0.001020284,0.9925168,0.0001368053,0.00009459593,0.00002098249,0.00005795562,0.0004723923,0.001382957],"genre_scores_gemma":[0.3546218,0.002351762,0.6323642,0.000163212,0.0003960678,0.000167906,0.0004743458,0.000332511,0.009128214],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00326887,"threshold_uncertainty_score":0.0117234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03852176666279102,"score_gpt":0.2462559422082981,"score_spread":0.2077341755455071,"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."}}