{"id":"W1592291313","doi":"10.1007/11430919_30","title":"Considering Re-occurring Features in Associative Classifiers","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Associative property; Computer science; Association rule learning; Artificial intelligence; Repetition (rhetorical device); Class (philosophy); Binary classification; Binary number; Association (psychology); Pattern recognition (psychology); Machine learning; Data mining; Support vector machine; Mathematics; Arithmetic","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"],"consensus_categories":[],"category_scores_codex":[0.0008795962,0.0004425916,0.0004951486,0.0007923811,0.0002429425,0.0006506356,0.002752186,0.0003094085,0.00001346517],"category_scores_gemma":[0.0001585584,0.0004398781,0.0000936822,0.0007498839,0.0004472769,0.0007532132,0.001050937,0.00117692,0.00003200871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006145433,"about_ca_system_score_gemma":0.0004763724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005710756,"about_ca_topic_score_gemma":0.0005013738,"domain_scores_codex":[0.9965413,0.00003086832,0.0005025078,0.001503557,0.0007320225,0.0006897241],"domain_scores_gemma":[0.997651,0.0006340572,0.0003065574,0.001133972,0.0001263943,0.0001480462],"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":[0.00000134104,0.00002495241,0.0001758287,0.00001232599,0.000009874995,0.00006159589,0.001921795,0.007971901,0.00003959619,0.02249382,0.0001465479,0.9671404],"study_design_scores_gemma":[0.0006299875,0.0001129079,0.002227968,0.001137717,0.00001196067,0.00007679142,0.000004686556,0.8763993,0.0008975095,0.1006032,0.01625652,0.001641447],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007019914,0.0003834986,0.9859583,0.001300604,0.0007130401,0.0002907417,0.000017887,0.0001499011,0.01111581],"genre_scores_gemma":[0.03541567,0.00005236315,0.960707,0.002587457,0.0005768258,0.00002941172,0.00001035381,0.00004042739,0.0005805056],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.965499,"threshold_uncertainty_score":0.9998053,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02490382803665742,"score_gpt":0.2723617080145319,"score_spread":0.2474578799778744,"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."}}