{"id":"W3015033384","doi":"10.18178/ijmlc.2020.10.1.898","title":"Gram-Schmidt Orthogonalization for Feature Ranking and Selection — A Case Study of Claim Prediction","year":2020,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Computing","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Universitas Indonesia","keywords":"Orthogonalization; Computer science; Ranking (information retrieval); Feature selection; Selection (genetic algorithm); Artificial intelligence; Feature (linguistics); n-gram; Data mining; Pattern recognition (psychology); Machine learning; Algorithm; Philosophy","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":[],"consensus_categories":[],"category_scores_codex":[0.0004237634,0.00007994549,0.0001522745,0.0001061684,0.0001515297,0.0001270036,0.0001423223,0.00003295757,8.979217e-7],"category_scores_gemma":[0.0001384203,0.00006783461,0.00004016142,0.0001291527,0.00001118458,0.0001990971,0.00009332565,0.0002479244,5.078223e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001317827,"about_ca_system_score_gemma":0.00001937381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003640277,"about_ca_topic_score_gemma":0.00000528261,"domain_scores_codex":[0.9991288,0.00009762181,0.0003065689,0.0001484012,0.0002447156,0.00007395511],"domain_scores_gemma":[0.9990402,0.0001319921,0.00043497,0.00002199569,0.0003196038,0.00005116684],"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":[0.0002615193,0.0002458669,0.43314,0.00006648397,0.0003799991,0.0003900401,0.01756854,0.08477563,0.0009326967,0.001621611,0.00008167332,0.4605359],"study_design_scores_gemma":[0.001434012,0.001131155,0.008369825,0.00004514338,0.00002586343,0.002471796,0.0006181406,0.9852378,0.00002256808,0.0001834066,0.0003939095,0.00006633224],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5931407,0.0001907766,0.4058602,0.0005782886,0.0001288652,0.0000674142,9.666368e-7,0.00001762858,0.00001512761],"genre_scores_gemma":[0.982785,0.00002058416,0.01679755,0.00009437124,0.0002910005,4.754402e-7,0.000002360499,0.000005246439,0.000003369342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9004622,"threshold_uncertainty_score":0.2766215,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01557466725338724,"score_gpt":0.2808721058992981,"score_spread":0.2652974386459109,"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."}}