{"id":"W2408772514","doi":"10.1016/j.asoc.2007.11.002","title":"Special issue on soft computing for dynamic data mining","year":2008,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Rough set; Granular computing; Computer science; Data mining; Soft computing; Forgetting; Big data; Redundancy (engineering); Computational intelligence; Interval (graph theory); Reduction (mathematics); Algorithm; Machine learning; Artificial intelligence; Artificial neural network; 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":[0.002300617,0.00171967,0.003853099,0.00275052,0.001376933,0.005477126,0.001788888,0.002450217,0.08621062],"category_scores_gemma":[0.006391346,0.0006543046,0.001427442,0.002966722,0.001066901,0.004489267,0.001759186,0.003020507,0.02649908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009949869,"about_ca_system_score_gemma":0.001273389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004413507,"about_ca_topic_score_gemma":0.0009811742,"domain_scores_codex":[0.9981663,0.0003904007,0.0001927365,0.0003547656,0.0007874099,0.0001085219],"domain_scores_gemma":[0.9935712,0.002716091,0.0002366021,0.0007446245,0.002029815,0.0007015694],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001043129,0.00008963198,0.0002313934,0.0008160225,0.00008898247,0.0001966443,0.00004336316,0.0009129997,0.0007820558,0.016385,0.8391184,0.1412312],"study_design_scores_gemma":[0.00003626458,0.0001505886,0.0007556642,0.0002539008,0.00007346811,0.0005967953,0.00005801124,0.008445184,0.0007272057,0.03246203,0.9564055,0.00003527096],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.003070456,0.05697568,0.07986737,0.02964832,0.7238671,0.0003924347,0.0008680792,0.001360607,0.10395],"genre_scores_gemma":[0.01834462,0.04617268,0.02273368,0.007242884,0.5972019,0.000436074,0.002312201,0.00137992,0.304176],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.08621062,"threshold_uncertainty_score":0.2884033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04558447279408514,"score_gpt":0.2805789724488535,"score_spread":0.2349944996547684,"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."}}