{"id":"W4285101299","doi":"10.1109/aiiot54504.2022.9817370","title":"An Approach for Automatic Discovery of Rules Based on ECG Data Using Learning Classifier Systems","year":2022,"lang":"en","type":"article","venue":"2022 IEEE World AI IoT Congress (AIIoT)","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Machine learning; Classifier (UML); Artificial intelligence; Component (thermodynamics); Personalized medicine; Decision support system; Association rule learning; Data mining; Bioinformatics","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":[],"consensus_categories":[],"category_scores_codex":[0.0008350111,0.0002142851,0.0003348986,0.0003142831,0.0008769973,0.0002721927,0.00212767,0.0000374169,0.0000263598],"category_scores_gemma":[0.00002794154,0.0002156758,0.00009272652,0.0007604407,0.00009123681,0.0006260668,0.0004564913,0.0003929193,0.000002472954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001652067,"about_ca_system_score_gemma":0.0002612029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008414604,"about_ca_topic_score_gemma":0.00000645919,"domain_scores_codex":[0.9974476,0.0003282956,0.0004558775,0.0008063299,0.0006148331,0.0003470537],"domain_scores_gemma":[0.9974745,0.0003385885,0.0003505912,0.001665935,0.00008097169,0.00008940496],"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":[0.00002495067,0.0007372818,0.0009942099,0.0001869896,0.00005838838,0.000005752368,0.0001166306,0.9577243,0.002435341,0.02752802,0.005342979,0.004845149],"study_design_scores_gemma":[0.0004378795,0.000109968,0.000366105,0.00004240035,0.00002810061,0.000007858667,0.0001570601,0.9919536,0.0001281067,0.0001405863,0.006370208,0.0002581128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01809992,0.0002299397,0.9780691,0.0003987849,0.001219982,0.0008751908,0.0005165018,0.0001989974,0.0003916357],"genre_scores_gemma":[0.8748832,0.000002439516,0.1211963,0.0002412414,0.0002604284,0.0005128584,0.0006125621,0.00004600626,0.00224493],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8568727,"threshold_uncertainty_score":0.8795006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05134884789720748,"score_gpt":0.3044739133264933,"score_spread":0.2531250654292858,"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."}}