{"id":"W4221101816","doi":"10.18280/ts.390123","title":"A Novel Ensemble Bagging Classification Method for Breast Cancer Classification Using Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"AI in cancer detection","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Overfitting; Machine learning; Artificial intelligence; Ensemble learning; Computer science; Breast cancer; Contextual image classification; Statistical classification; Weighted voting; Pattern recognition (psychology); Cancer; Voting; Image (mathematics); Artificial neural network; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001204099,0.0001741813,0.0001671893,0.0002256555,0.0008394711,0.0001293795,0.0004862516,0.00004530762,0.0001356094],"category_scores_gemma":[0.00000952764,0.0001947757,0.00009197186,0.0005178349,0.00002104256,0.0004424618,0.0001612703,0.0002729211,0.000001048505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009571799,"about_ca_system_score_gemma":0.000158163,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003622054,"about_ca_topic_score_gemma":0.00003154421,"domain_scores_codex":[0.9981406,0.0001789437,0.0003608909,0.0005599506,0.0004556209,0.0003039828],"domain_scores_gemma":[0.9990859,0.0001204748,0.0003460831,0.0002513127,0.0001374544,0.00005875316],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004916703,0.00008100284,0.0003465336,0.00002457285,0.00002530621,3.912822e-7,0.000395317,0.007628196,0.7157377,0.004168296,0.0000711277,0.2714724],"study_design_scores_gemma":[0.0004151782,0.0001274586,0.001977367,0.00001955348,0.00002881509,0.00005966375,0.0001387384,0.9581991,0.03021734,0.0003613515,0.008230114,0.0002253387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002710318,0.00007355808,0.9939865,0.001773052,0.0002586273,0.0006480895,0.00005952626,0.0003979004,0.00009243682],"genre_scores_gemma":[0.591027,0.000009667056,0.4074917,0.0002578329,0.0002015247,0.0009034425,0.00001865642,0.0000270231,0.0000631559],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9505709,"threshold_uncertainty_score":0.7942724,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06985194399396133,"score_gpt":0.3282756140508642,"score_spread":0.2584236700569029,"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."}}