{"id":"W3215087645","doi":"10.1155/2021/5069016","title":"A Novel Rank Aggregation‐Based Hybrid Multifilter Wrapper Feature Selection Method in Software Defect Prediction","year":2021,"lang":"en","type":"article","venue":"Computational Intelligence and Neuroscience","topic":"Software Engineering Research","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Feature selection; Rank (graph theory); Computer science; Feature (linguistics); Software; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence; Data mining; Machine learning; Mathematics","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":[0.0004436522,0.0001610582,0.0001401491,0.0003263138,0.0001976459,0.0002899157,0.0004171959,0.00005405749,0.000007070733],"category_scores_gemma":[0.001690501,0.0001667424,0.00006327767,0.001724739,0.00009137217,0.0006571545,0.0001635527,0.0003168624,0.000008005338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007131492,"about_ca_system_score_gemma":0.0002376884,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001203635,"about_ca_topic_score_gemma":0.000005550487,"domain_scores_codex":[0.9979874,0.000114414,0.000228069,0.0007990632,0.0005611535,0.000309851],"domain_scores_gemma":[0.9980293,0.001264815,0.00005428502,0.0002277133,0.0003100286,0.0001138882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007109274,0.00009534092,0.01194374,0.00002482975,0.000002367339,0.00003172328,0.0001210023,0.9520729,0.006271576,0.0008208073,0.0001062011,0.02850243],"study_design_scores_gemma":[0.000147495,0.00005233162,0.06441891,0.00004970379,0.000002200771,0.0002369637,0.000005334922,0.899407,0.03420044,0.001135042,0.0001996928,0.0001448728],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01705997,0.0001433614,0.9815186,0.000525497,0.0003918742,0.0001635264,0.00001080401,0.0001802282,0.000006198821],"genre_scores_gemma":[0.6475624,0.0000145168,0.3517289,0.0005474389,0.00002902593,0.00002835045,0.000009554679,0.000009181579,0.00007061353],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6305025,"threshold_uncertainty_score":0.6799558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04203692543679134,"score_gpt":0.3140236752147824,"score_spread":0.2719867497779911,"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."}}