{"id":"W1910978492","doi":"10.1111/coin.12071","title":"Mining Evolving Data Streams with Particle Filters","year":2015,"lang":"en","type":"article","venue":"Computational Intelligence","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Resampling; Computer science; Data stream; Data stream mining; Particle filter; Logistic regression; Data mining; Gradient descent; Artificial intelligence; Key (lock); Feature (linguistics); Noise (video); Filter (signal processing); Pattern recognition (psychology); Regression; Machine learning; Artificial neural network; Mathematics; Statistics; Kalman filter","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":[0.002505949,0.001048242,0.001536763,0.001883057,0.0004829103,0.001358261,0.002011291,0.001453439,0.000657675],"category_scores_gemma":[0.009779128,0.000920961,0.001212441,0.001817727,0.0005807354,0.002032562,0.0009983074,0.001923253,0.0003392586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007772004,"about_ca_system_score_gemma":0.001064771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0071818,"about_ca_topic_score_gemma":0.004572781,"domain_scores_codex":[0.9991691,0.0001890029,0.00007528885,0.0002494438,0.0002535095,0.000063748],"domain_scores_gemma":[0.996311,0.002275106,0.0004430589,0.0002743661,0.0005969832,0.00009954323],"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":[0.0001136398,0.00009938968,0.005481013,0.0001174683,0.0001538619,0.0001798805,0.0001450894,0.8187918,0.002528223,0.006965954,0.002452438,0.1629712],"study_design_scores_gemma":[0.000004354773,0.000006731689,0.0001189307,0.00000275134,0.000003421055,0.000010172,0.000004720957,0.9976495,0.0002844981,0.001672236,0.0002394421,0.00000323329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01036471,0.0001814059,0.9885733,0.0001484245,0.00003587463,0.00003219003,0.00006891401,0.0004112982,0.0001838406],"genre_scores_gemma":[0.3477226,0.0006061444,0.6485186,0.0002411051,0.0001882074,0.0002550094,0.0008447477,0.0001224007,0.001501225],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0071818,"threshold_uncertainty_score":0.01427996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1541202639526685,"score_gpt":0.3415073941507162,"score_spread":0.1873871301980476,"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."}}