{"id":"W1538131118","doi":"10.1007/11548706_38","title":"Relevant Attribute Discovery in High Dimensional Data Based on Rough Sets and Unsupervised Classification: Application to Leukemia Gene Expressions","year":2005,"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":"National Research Council Canada","funders":"","keywords":"Rough set; Artificial intelligence; Computer science; Pattern recognition (psychology); High dimensional; Data mining","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":[0.003571439,0.0006119667,0.0022142,0.00208714,0.0007261685,0.002178783,0.001086018,0.0008102209,0.0006825085],"category_scores_gemma":[0.009107933,0.0004006776,0.001834001,0.003820945,0.0009504402,0.001451342,0.001106887,0.00190063,0.0002443269],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006883899,"about_ca_system_score_gemma":0.0008491716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009899762,"about_ca_topic_score_gemma":0.001386005,"domain_scores_codex":[0.9983149,0.0006107871,0.0001405421,0.0001624644,0.0007126931,0.00005857304],"domain_scores_gemma":[0.9941409,0.004791987,0.0002367394,0.0002912128,0.0004830381,0.00005600683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003407735,0.0004018825,0.003654396,0.0006650048,0.0002334758,0.0005311871,0.001253991,0.1827621,0.01659645,0.05952964,0.005609245,0.7284219],"study_design_scores_gemma":[0.00002546562,0.0000818491,0.001933927,0.00003287297,0.00007457956,0.0002884181,0.0001746697,0.9064063,0.006836522,0.08150306,0.002593215,0.00004914734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03223239,0.001356423,0.9642753,0.0004003733,0.00006153905,0.0000683746,0.0001236358,0.0003171001,0.001164802],"genre_scores_gemma":[0.1773552,0.001436559,0.8193228,0.00007671885,0.000124761,0.0001353978,0.0002481837,0.00006414956,0.001236259],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003571439,"threshold_uncertainty_score":0.01888776,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03730734188509197,"score_gpt":0.2665162858400922,"score_spread":0.2292089439550003,"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."}}