{"id":"W2963164464","doi":"","title":"Ranking to Learn: Feature Ranking and Selection via Eigenvector Centrality","year":2017,"lang":"en","type":"article","venue":"ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam)","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Technische Universität Dortmund; Institut National de Recherche en Sciences et Technologies pour l'Environnement et l'Agriculture; Vysoká Škola Ekonomická v Praze; National Research Council Canada; Indian Council of Agricultural Research; Università degli Studi di Torino; Universita degli Studi di Bari Aldo Moro; Politechnika Warszawska; University of Oregon; Universidade do Porto; Politechnika Poznańska; Università degli Studi di Ferrara; University of Ottawa","keywords":"Centrality; Feature selection; Computer science; Graph; Dimensionality reduction; Ranking (information retrieval); Artificial intelligence; Data mining; Pattern recognition (psychology); Filter (signal processing); Machine learning; Mathematics; Theoretical computer science; Computer vision","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001420558,0.000384884,0.0004126948,0.0002517322,0.002817136,0.001794557,0.001349042,0.0001702119,0.00001000197],"category_scores_gemma":[0.0004033678,0.0003545153,0.000156344,0.0003019569,0.00007529248,0.0006913259,0.0004657729,0.001010736,0.00003449518],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008707699,"about_ca_system_score_gemma":0.00007948404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002161639,"about_ca_topic_score_gemma":0.00003598077,"domain_scores_codex":[0.997173,0.000292493,0.0003672975,0.0009018117,0.0004938022,0.000771665],"domain_scores_gemma":[0.9982932,0.0001459088,0.0003658607,0.0007290172,0.0001495103,0.0003165713],"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":[0.0001691578,0.000228846,0.08119022,0.0001720172,0.0003541144,0.0003165089,0.01861028,0.002201784,0.1176162,0.008997814,0.001817796,0.7683253],"study_design_scores_gemma":[0.005354774,0.00116064,0.2581916,0.001601553,0.00023228,0.001949164,0.0003155324,0.5916305,0.05840475,0.009966046,0.06705708,0.004135993],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6634904,0.0003234709,0.325763,0.007599363,0.001284337,0.0002955378,0.000002865946,0.000476179,0.0007649134],"genre_scores_gemma":[0.9526848,0.00004477812,0.04515263,0.0004687983,0.0007373847,0.00000778515,0.000003283852,0.0000392213,0.0008613851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7641893,"threshold_uncertainty_score":0.9998907,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01112636185516138,"score_gpt":0.2594980136220977,"score_spread":0.2483716517669363,"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."}}