{"id":"W3092057871","doi":"10.1007/978-3-030-61834-6_3","title":"IRBASIR-B: Rule Induction from Similarity Relations, a Bayesian Approach","year":2020,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Similarity (geometry); Computer science; Bayesian probability; Rule induction; Set (abstract data type); Bayes' theorem; Data mining; Artificial intelligence; Scheme (mathematics); Construct (python library); Process (computing); Algorithm; Pattern recognition (psychology); Machine learning; Mathematics","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":[0.003500603,0.001043515,0.002267404,0.002376174,0.0009538061,0.002572667,0.003282489,0.001510032,0.01455875],"category_scores_gemma":[0.01012345,0.001046728,0.001835239,0.003244697,0.001145289,0.004221148,0.00221597,0.003221026,0.007245468],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007771094,"about_ca_system_score_gemma":0.001656618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003130607,"about_ca_topic_score_gemma":0.003927493,"domain_scores_codex":[0.996335,0.001067803,0.0002794658,0.0006719898,0.001521296,0.0001244011],"domain_scores_gemma":[0.9966227,0.001895006,0.0001088033,0.0004516414,0.0008403002,0.00008151482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002172281,0.0001643304,0.0003322548,0.0004987636,0.0001344495,0.0001135273,0.0001385146,0.02131899,0.002725654,0.08707901,0.03674418,0.8505332],"study_design_scores_gemma":[0.00009671524,0.0001188184,0.0006394741,0.0001917734,0.0001366647,0.0002876605,0.00005496953,0.5147425,0.009068646,0.432065,0.04251691,0.00008092939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001061898,0.0007579374,0.9908545,0.0002142226,0.0001310776,0.0001011224,0.0003050984,0.002052173,0.004521962],"genre_scores_gemma":[0.02635853,0.0007175975,0.9648722,0.0002724036,0.0001581554,0.0002321368,0.001160147,0.0005026957,0.005726065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01455875,"threshold_uncertainty_score":0.04870391,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0524866287513743,"score_gpt":0.2628876335784501,"score_spread":0.2104010048270758,"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."}}