{"id":"W3187962531","doi":"10.48550/arxiv.2108.02932","title":"Incremental Feature Learning For Infinite Data","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Feature (linguistics); Transfer of learning; Database transaction; Artificial intelligence; Machine learning; Incremental learning; Credit card fraud; Credit card; Database","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":[],"category_scores_codex":[0.0003823735,0.0002536702,0.0002703511,0.0001898753,0.0001974532,0.0003368334,0.004702955,0.0003053302,0.00001531105],"category_scores_gemma":[0.0001654234,0.0003183176,0.0001036311,0.0004613722,0.0000658809,0.001019245,0.008628629,0.0007643498,0.00001614379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001744581,"about_ca_system_score_gemma":0.0002752652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004426362,"about_ca_topic_score_gemma":0.00002687874,"domain_scores_codex":[0.9978027,0.0001346888,0.0001612063,0.001515243,0.00009753322,0.000288589],"domain_scores_gemma":[0.9960608,0.0001360814,0.0002972213,0.003202537,0.0002073636,0.00009600658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001791188,0.0007857132,0.01995802,0.001337402,0.001049535,0.0009328305,0.001454867,0.05991261,0.006315609,0.7639214,0.1051318,0.03902106],"study_design_scores_gemma":[0.0005188167,0.00005584461,0.001588343,0.0001719156,0.00007254221,0.000007943384,0.0001859498,0.9468207,0.002136306,0.006554476,0.04116023,0.0007269842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00801872,0.00006274778,0.9892721,0.0002955463,0.0003347127,0.0003913533,0.0001450208,0.0006055592,0.0008742318],"genre_scores_gemma":[0.9127012,0.0002287594,0.08278546,0.0001858583,0.00008450657,0.000003722128,0.002379124,0.00002214299,0.001609188],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9064866,"threshold_uncertainty_score":0.9999269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1454533410690701,"score_gpt":0.233143527923803,"score_spread":0.08769018685473284,"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."}}