{"id":"W2914754426","doi":"10.1007/978-3-030-12598-1_44","title":"Opposition-Based Multi-objective Binary Differential Evolution for Multi-label Feature Selection","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Feature selection; Computer science; Artificial intelligence; Differential evolution; Binary number; Preprocessor; Binary classification; Pattern recognition (psychology); Machine learning; Feature (linguistics); Pareto principle; Optimization problem; Mathematical optimization; Algorithm; Support vector machine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001476779,0.0006893681,0.001431876,0.0007669795,0.0003818576,0.0007968773,0.001539048,0.001370437,0.00250288],"category_scores_gemma":[0.002343804,0.0004214493,0.0009560739,0.0009446191,0.000557419,0.0006042494,0.001356056,0.001256027,0.0004369006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007651435,"about_ca_system_score_gemma":0.000534553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002781761,"about_ca_topic_score_gemma":0.002401514,"domain_scores_codex":[0.9995558,0.0001548762,0.00002546147,0.00007179056,0.0001457402,0.00004636836],"domain_scores_gemma":[0.9990861,0.000616334,0.00005276086,0.00003724513,0.0001778326,0.00002972609],"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":[0.0001510344,0.00014962,0.0005442174,0.0001690231,0.0001017799,0.00009636369,0.0001156959,0.7416028,0.007050744,0.01281906,0.002266256,0.2349333],"study_design_scores_gemma":[0.000004843985,0.00001967938,0.00004291523,0.000003619127,0.000004697303,0.000009352782,0.000002373233,0.998746,0.000259139,0.0007197289,0.0001849517,0.000002556716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01061148,0.0002488335,0.9866174,0.0000867628,0.00005786564,0.00004239731,0.00002956311,0.0001743388,0.002131398],"genre_scores_gemma":[0.5393884,0.0003028452,0.4515728,0.0002496148,0.00008464503,0.0005097916,0.0002608248,0.0001532599,0.007477817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002781761,"threshold_uncertainty_score":0.008373022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03066137713629348,"score_gpt":0.2787367194164769,"score_spread":0.2480753422801834,"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."}}