{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00375346,0.0007728861,0.001248586,0.001203142,0.001280219,0.001004628,0.002191028,0.001441924,0.00159002],"category_scores_gemma":[0.01286405,0.0003803779,0.0008087418,0.0007492983,0.0005959302,0.002175404,0.00138239,0.001601508,0.0005548577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009103406,"about_ca_system_score_gemma":0.001661721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005154857,"about_ca_topic_score_gemma":0.009597021,"domain_scores_codex":[0.9989504,0.0002617615,0.00007605456,0.0002852086,0.0002583288,0.0001682115],"domain_scores_gemma":[0.9959155,0.002009882,0.000237086,0.0005995042,0.001041912,0.0001960985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005114623,0.0001826975,0.008136653,0.0001094505,0.0001209176,0.000154416,0.0002237228,0.5467588,0.007358931,0.01016232,0.008835535,0.4174451],"study_design_scores_gemma":[0.0000121666,0.00001998904,0.0003522657,0.0000102056,0.00000989098,0.00003781352,0.00001492252,0.9940084,0.001252187,0.003674486,0.0005997181,0.000007986261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07279263,0.0007060293,0.9221292,0.0002889073,0.0001665382,0.0001015424,0.0001820802,0.001933154,0.001699997],"genre_scores_gemma":[0.5541571,0.0002521965,0.4421801,0.000345728,0.0001652989,0.0002664455,0.0007754272,0.0003343008,0.001523355],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005154857,"threshold_uncertainty_score":0.01985037,"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."}}