{"id":"W2042336188","doi":"10.1109/cec.2009.4983151","title":"Techniques for evolutionary rule discovery in data mining","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; K-optimal pattern discovery; Business process discovery; Knowledge extraction; Evolutionary computation; Process (computing); Task (project management); Data mining; Evolutionary algorithm; Machine learning; Artificial intelligence; Data science; Work in process; Engineering","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":[0.01100605,0.001318912,0.002047407,0.005651767,0.001286536,0.002011267,0.003469488,0.002093576,0.002601961],"category_scores_gemma":[0.03414844,0.001065874,0.003089271,0.006217331,0.002010084,0.00276807,0.002841491,0.003576093,0.00120341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008756004,"about_ca_system_score_gemma":0.001176475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001650441,"about_ca_topic_score_gemma":0.001990018,"domain_scores_codex":[0.9917092,0.003628422,0.0007057444,0.0008651189,0.002902616,0.0001890063],"domain_scores_gemma":[0.9806147,0.01580364,0.0006291763,0.001387728,0.001431955,0.0001327138],"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":[0.00009890076,0.0002675023,0.0029288,0.0008866498,0.0006663258,0.0006374938,0.0007363076,0.1456552,0.003273672,0.1530212,0.00403185,0.6877961],"study_design_scores_gemma":[0.00009870126,0.0002275887,0.001105229,0.0003419688,0.0002027458,0.001424439,0.0001345331,0.7350451,0.005578957,0.2273498,0.0284023,0.0000887036],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001407841,0.0009345757,0.9960375,0.0001859807,0.00003817042,0.0001046352,0.0000285389,0.0002244609,0.001038368],"genre_scores_gemma":[0.02437091,0.001032353,0.9729801,0.0001456822,0.00006139188,0.0002873172,0.0001142581,0.00006544893,0.0009425702],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01100605,"threshold_uncertainty_score":0.05820626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03311982567617482,"score_gpt":0.301686815609515,"score_spread":0.2685669899333402,"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."}}