{"id":"W4320024183","doi":"10.1109/bigdata55660.2022.10020511","title":"Dynamic Ensemble Size Adjustment for Memory Constrained Mondrian Forest","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Big Data (Big Data)","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Mondrian; Overfitting; Computer science; Machine learning; Ensemble learning; Tree (set theory); Data stream mining; Artificial intelligence; Memory model; Data mining; Shared memory; Parallel computing; Mathematics; Artificial neural network","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.001104554,0.0003629104,0.0003348027,0.0002935242,0.000354318,0.0004384728,0.01665491,0.00007283949,0.0003061126],"category_scores_gemma":[0.0004513334,0.0003953897,0.00006487243,0.0003132719,0.0001396113,0.001256034,0.008948982,0.0004483721,0.00005597885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002919731,"about_ca_system_score_gemma":0.0006375826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002150081,"about_ca_topic_score_gemma":0.0007148937,"domain_scores_codex":[0.9957039,0.0001601081,0.0005779085,0.001809565,0.001251207,0.0004973239],"domain_scores_gemma":[0.9930424,0.0005188845,0.0003809723,0.005703775,0.0001917166,0.0001622677],"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":[0.0002322014,0.0005641511,0.0000526066,0.00002909042,0.0002432512,0.0001082436,0.0001664618,0.00009098757,0.004775104,0.09421953,0.27284,0.6266783],"study_design_scores_gemma":[0.002184832,0.0009827001,0.0004354224,0.0001033891,0.00006230748,0.0001750359,0.000423076,0.7270855,0.001188846,0.01414893,0.2521634,0.001046545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002743004,0.00008416266,0.8068509,0.008618482,0.01637206,0.001592661,0.152112,0.0008211672,0.0108055],"genre_scores_gemma":[0.827562,0.000179109,0.08799274,0.002025427,0.0006845673,0.000689582,0.07883611,0.00006373208,0.00196673],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.824819,"threshold_uncertainty_score":0.9998498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2162287159834481,"score_gpt":0.3500891358264674,"score_spread":0.1338604198430194,"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."}}