{"id":"W1585793187","doi":"10.1007/978-3-642-21881-1_35","title":"Incorporating Game Theory in Feature Selection for Text Categorization","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Categorization; Feature selection; Text categorization; Computer science; Selection (genetic algorithm); Feature (linguistics); Artificial intelligence; Demonstrative; Game theory; Machine learning; Natural language processing; Information retrieval; Mathematics; Linguistics; Mathematical economics","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":[0.0009649657,0.0003777353,0.0003464915,0.001202282,0.0001731266,0.000378699,0.002098169,0.0004822994,0.000007961402],"category_scores_gemma":[0.0001948239,0.0003466964,0.00007792185,0.0009614319,0.000372751,0.0008698502,0.0005102073,0.0006354887,0.00001488639],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003453901,"about_ca_system_score_gemma":0.0003836188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001426312,"about_ca_topic_score_gemma":0.0001258879,"domain_scores_codex":[0.9975179,0.00003491507,0.0004162821,0.001185283,0.0004085332,0.0004370678],"domain_scores_gemma":[0.9982017,0.0003446111,0.0004186181,0.0007472768,0.0002302289,0.0000575703],"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":[0.00000634555,0.00001470991,0.0001884296,0.00001787012,0.000002536882,0.00000200767,0.0003755254,0.001617544,0.0001547711,0.495373,0.00001835388,0.5022289],"study_design_scores_gemma":[0.0002164622,0.0001413292,0.0003077201,0.0001276345,0.000003556857,0.00001187078,5.022703e-7,0.1505186,0.004535016,0.8429577,0.0007827086,0.0003969065],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0000359481,0.0002543754,0.9953399,0.0007456852,0.0007776011,0.000633719,0.000002096905,0.0003660611,0.001844638],"genre_scores_gemma":[0.594824,0.00003459291,0.4031076,0.0004861832,0.0002239508,0.00007495829,0.00001529566,0.00003694016,0.00119647],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5947881,"threshold_uncertainty_score":0.9998985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02111208103021165,"score_gpt":0.2447933497136184,"score_spread":0.2236812686834068,"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."}}