{"id":"W1571835199","doi":"10.1007/978-3-540-68123-6_33","title":"Effectiveness of Fuzzy Discretization for Class Association Rule-Based Classification","year":2008,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Association rule learning; Classifier (UML); Data mining; Artificial intelligence; Fuzzy logic; Computer science; Machine learning; Discretization; Pattern recognition (psychology); Support vector machine; Fuzzy set; Feature vector; Fuzzy rule; Fuzzy classification; 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":[0.003902969,0.00044231,0.001067349,0.001338408,0.0005583631,0.001891206,0.001007312,0.0009110924,0.001678238],"category_scores_gemma":[0.0148956,0.0002419692,0.0005249467,0.001201229,0.0005433526,0.001713596,0.0007567966,0.0008199704,0.0003778489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007556339,"about_ca_system_score_gemma":0.0007453947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002977976,"about_ca_topic_score_gemma":0.002111004,"domain_scores_codex":[0.996932,0.00119806,0.0002095121,0.0003196845,0.001236472,0.0001042252],"domain_scores_gemma":[0.9848219,0.01215861,0.0002710491,0.001325514,0.001260835,0.000162112],"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":[0.001563912,0.0003230852,0.005924676,0.0003678692,0.0002129431,0.0002076549,0.000285224,0.1536522,0.009975153,0.030247,0.005100553,0.7921398],"study_design_scores_gemma":[0.00005661546,0.0001136935,0.001428987,0.00003676264,0.00007931422,0.0002276402,0.00009604125,0.9699358,0.004686109,0.02203764,0.001283645,0.00001779549],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1460353,0.004038007,0.8370138,0.0007583891,0.0003701399,0.0001233539,0.0005164421,0.0008384761,0.01030605],"genre_scores_gemma":[0.6895542,0.0009422242,0.3070459,0.0001679827,0.0001440038,0.00006558939,0.0004752109,0.0000546002,0.001550327],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003902969,"threshold_uncertainty_score":0.02064115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01916779481036609,"score_gpt":0.2594617264196813,"score_spread":0.2402939316093152,"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."}}