{"id":"W4295767686","doi":"10.1109/fuzz-ieee55066.2022.9882660","title":"Cutting down high dimensional data with Fuzzy weighted forests (FWF)","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)","topic":"Fuzzy Logic and Control Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"China Scholarship Council; Queen's University","keywords":"Interpretability; Fuzzy logic; Data mining; Fuzzy rule; Mathematics; Pruning; Fuzzy number; Curse of dimensionality; Tree (set theory); Computer science; Artificial intelligence; Pattern recognition (psychology); Algorithm; Fuzzy set; Combinatorics; Botany","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002451653,0.0009632614,0.001379678,0.002233033,0.0008762176,0.00105887,0.001084461,0.001095755,0.0007390555],"category_scores_gemma":[0.00603309,0.0005884338,0.001823375,0.001977467,0.0006006145,0.002956516,0.0009914435,0.001330545,0.0003255388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005655271,"about_ca_system_score_gemma":0.001009242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00944238,"about_ca_topic_score_gemma":0.01204708,"domain_scores_codex":[0.998894,0.0002830373,0.000116462,0.0002126749,0.0004111733,0.00008265529],"domain_scores_gemma":[0.9975406,0.001502495,0.0002274087,0.0003132006,0.0003724189,0.00004389615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001350326,0.00008864629,0.004207128,0.0002285739,0.0002319646,0.0002817274,0.0002970317,0.6451045,0.008591091,0.01446641,0.001176723,0.3251912],"study_design_scores_gemma":[0.000005745388,0.00003256021,0.0004021096,0.00002416148,0.00002607638,0.00007251828,0.0000316306,0.9806432,0.001845615,0.01588784,0.001012548,0.00001600654],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01243604,0.0003107873,0.9865547,0.00005684627,0.00002689569,0.00003373624,0.00006131221,0.0002176046,0.0003020706],"genre_scores_gemma":[0.2440589,0.0004965898,0.7541618,0.00008997488,0.00005256178,0.0001215587,0.0003059468,0.00006300787,0.0006497651],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00944238,"threshold_uncertainty_score":0.01877487,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04867813566072707,"score_gpt":0.2730070252752128,"score_spread":0.2243288896144857,"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."}}