{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002336262,0.001462711,0.002349609,0.003606018,0.001243745,0.002627315,0.002285946,0.001803795,0.004535223],"category_scores_gemma":[0.01307545,0.0008394823,0.001585674,0.003345442,0.000965922,0.003480008,0.001838957,0.002353069,0.002306212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009315365,"about_ca_system_score_gemma":0.001874265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00601142,"about_ca_topic_score_gemma":0.009256029,"domain_scores_codex":[0.9980415,0.0007410475,0.00009511432,0.0003835901,0.0005208955,0.0002178397],"domain_scores_gemma":[0.9945104,0.003807807,0.0002132952,0.0005550436,0.0007111803,0.0002021793],"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":[0.0004910051,0.0004894973,0.003569055,0.000277695,0.000272841,0.0002163923,0.0002253502,0.1505108,0.005048857,0.03314804,0.04162333,0.7641271],"study_design_scores_gemma":[0.00006755959,0.00008906342,0.000635365,0.00002289468,0.0000513308,0.00007922975,0.00005941408,0.9467056,0.001690746,0.04750682,0.003061347,0.00003062981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03020424,0.00134067,0.9626785,0.0008335713,0.000222439,0.0001214223,0.0005027351,0.001924922,0.002171521],"genre_scores_gemma":[0.3979136,0.001134562,0.5839291,0.000435735,0.0006422944,0.0003349914,0.003610073,0.0009485469,0.01105097],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00601142,"threshold_uncertainty_score":0.01517183,"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."}}