{"id":"W3153416049","doi":"10.1007/s13042-021-01291-y","title":"A novel binary many-objective feature selection algorithm for multi-label data classification","year":2021,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Feature selection; Computer science; Multi-label classification; Feature (linguistics); Pattern recognition (psychology); Artificial intelligence; Computational intelligence; Data mining; Computational complexity theory; Binary number; Benchmarking; Binary classification; Selection (genetic algorithm); Machine learning; Algorithm; Mathematics; Support vector machine","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":[0.000330337,0.0001054047,0.0001307788,0.0001655701,0.0001015249,0.0002493367,0.0007582366,0.00008780522,0.000005095152],"category_scores_gemma":[0.0004036388,0.00009602329,0.00004163591,0.0001720226,0.00003790356,0.0004422852,0.0003070683,0.0003859866,0.000001777665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005932631,"about_ca_system_score_gemma":0.00009949885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007514911,"about_ca_topic_score_gemma":0.000006178872,"domain_scores_codex":[0.9990086,0.0000479275,0.0002613659,0.0002583561,0.0003141809,0.0001095905],"domain_scores_gemma":[0.9984832,0.0001336147,0.000396226,0.0001676572,0.0007756263,0.00004360671],"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":[0.00002343773,0.0003056642,0.002763121,0.000006973824,0.0001687753,0.00001411073,0.0002366157,0.00007565556,0.01235188,0.01281966,0.0009456594,0.9702885],"study_design_scores_gemma":[0.001259596,0.0001520544,0.006831858,0.00003509497,0.00002233849,0.0003849687,0.0001889955,0.9630537,0.001452411,0.0006199558,0.02588707,0.0001119357],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002250799,0.001060585,0.9908525,0.005152518,0.0004706122,0.00005891377,0.0000240633,0.00006366579,0.00006637818],"genre_scores_gemma":[0.1156564,0.0006596193,0.8811882,0.0001233302,0.000193067,0.000004511655,0.0001093848,0.00001208599,0.002053396],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9701765,"threshold_uncertainty_score":0.3915716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05021316710036461,"score_gpt":0.3376043278010533,"score_spread":0.2873911607006887,"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."}}