{"id":"W97148157","doi":"10.4018/978-1-59140-051-6.ch006","title":"Data Mining Based on Rough Sets","year":2003,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Rough set; Generalization; Data mining; Set (abstract data type); Computer science; Context (archaeology); Data set; Dominance-based rough set approach; Artificial intelligence; Mathematics; Geography","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002513208,0.0005347126,0.0004647672,0.000072184,0.0001524612,0.0003283684,0.003159456,0.0004026484,0.00002849299],"category_scores_gemma":[0.00002732449,0.0004808226,0.0001442011,0.00003451809,0.00007248112,0.0001365072,0.0007682803,0.0003169655,0.0003107056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000154609,"about_ca_system_score_gemma":0.0002543843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001752275,"about_ca_topic_score_gemma":0.00002194731,"domain_scores_codex":[0.9971449,0.00003814399,0.0003889092,0.001271321,0.0006677196,0.000489029],"domain_scores_gemma":[0.9959626,0.00007333152,0.0002412254,0.003462453,0.00006061869,0.000199781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008099602,0.00001329137,0.000003571587,0.0000137147,0.00002760558,0.0002559965,0.00001560749,0.00004869733,2.039758e-7,0.9108382,0.04808944,0.0406855],"study_design_scores_gemma":[0.0006279357,0.0003000842,0.00002013305,0.0003260831,0.00005147668,0.00007147204,0.000002254393,0.02830463,0.000003557967,0.2664235,0.702767,0.0011019],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"other","genre_gemma":"methods","genre_scores_codex":[0.000002811677,0.0001799619,0.01287581,0.0003363174,0.001047824,0.0002517141,0.0004683885,0.000233554,0.9846036],"genre_scores_gemma":[0.05784274,0.00003060037,0.6490006,0.08991922,0.001844041,0.00005969251,0.000476102,0.0003847815,0.2004422],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.7841614,"threshold_uncertainty_score":0.9997643,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06111463847584939,"score_gpt":0.2744266181367969,"score_spread":0.2133119796609475,"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."}}