{"id":"W4281624530","doi":"10.1007/s10489-022-03696-w","title":"A Q-learning approach to attribute reduction","year":2022,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Reduct; Computer science; Rough set; Artificial intelligence; Reduction (mathematics); Construct (python library); Set (abstract data type); Reinforcement learning; Machine learning; Scheme (mathematics); State (computer science); Data mining; Mathematics; Algorithm","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":[0.005742759,0.0006776517,0.002059536,0.002018084,0.001095715,0.002817996,0.003149389,0.001329014,0.005793171],"category_scores_gemma":[0.01469983,0.000581257,0.002250955,0.003663733,0.001935636,0.00308594,0.002813917,0.002881543,0.001050194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00131301,"about_ca_system_score_gemma":0.002389371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004695508,"about_ca_topic_score_gemma":0.003332403,"domain_scores_codex":[0.99459,0.002990849,0.0002830636,0.0006060132,0.00137767,0.0001523747],"domain_scores_gemma":[0.9935371,0.00439197,0.0001508972,0.0006289349,0.001169864,0.0001211944],"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":[0.000107605,0.0002548004,0.0008134106,0.0003726495,0.0002562783,0.0001199058,0.0003661479,0.1232015,0.001101949,0.5164245,0.006075552,0.3509057],"study_design_scores_gemma":[0.00004021075,0.00007689218,0.0002203505,0.00004903269,0.00005403214,0.00007414095,0.00006117793,0.4939084,0.0005407186,0.4997967,0.005149041,0.00002929666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009065137,0.0002210708,0.9973987,0.0002663948,0.00004051924,0.00003895594,0.00003425038,0.00006026115,0.001033326],"genre_scores_gemma":[0.1111794,0.0008195086,0.8831985,0.0003588266,0.0002331881,0.000295718,0.0002318328,0.0000628029,0.003620142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005793171,"threshold_uncertainty_score":0.03037095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02969355596337938,"score_gpt":0.2403125882135875,"score_spread":0.2106190322502081,"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."}}