{"id":"W2143985241","doi":"10.1109/nafips.2004.1336252","title":"Rough set approximations in formal concept analysis","year":2004,"lang":"en","type":"article","venue":"IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04.","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Rough set; Formal concept analysis; Set (abstract data type); Approximations of π; Computer science; Universal set; Mathematics; Set theory; Dominance-based rough set approach; Approximation theory; Lattice (music); Algebra over a field; Discrete mathematics; Theoretical computer science; Algorithm; Artificial intelligence; Applied mathematics; Pure mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.001100407,0.0002825269,0.0004201249,0.0005435105,0.0005807927,0.000370815,0.00162198,0.0001592196,0.000003264026],"category_scores_gemma":[0.0002737679,0.0002136963,0.000223833,0.003844762,0.000170661,0.006760294,0.0002322194,0.0003269109,0.00002570367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001668708,"about_ca_system_score_gemma":0.0004235404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003131746,"about_ca_topic_score_gemma":0.00005268572,"domain_scores_codex":[0.9970923,0.00009797725,0.001193763,0.0002726016,0.0007454671,0.0005979077],"domain_scores_gemma":[0.9977031,0.00008099751,0.0009009097,0.0006672923,0.0005517321,0.00009591399],"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":[0.00002644803,0.0001684556,0.002170618,0.0002508653,0.0001436244,0.000002504905,0.07715246,0.8752466,0.00003397259,0.009100003,0.001629426,0.03407501],"study_design_scores_gemma":[0.01549273,0.001182342,0.05825832,0.003603704,0.00134777,0.0002715598,0.02936042,0.7314994,0.0192873,0.1140507,0.01992971,0.005716023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.310241,0.001159446,0.639571,0.006324891,0.001610913,0.001679402,0.0002995263,0.0007595597,0.03835434],"genre_scores_gemma":[0.9666371,0.000007845241,0.03252256,0.0006451061,0.00007495985,0.00003117115,0.00003518123,0.000009536941,0.00003654087],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6563962,"threshold_uncertainty_score":0.8714283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01338780521144156,"score_gpt":0.2474960727570976,"score_spread":0.2341082675456561,"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."}}