{"id":"W4287218407","doi":"10.5539/cis.v15n3p47","title":"Homogenous Multiple Classifier System for Software Quality Assessment Based on Support Vector Machine","year":2022,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Software Reliability and Analysis Research","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Tertiary Education Trust Fund","keywords":"Support vector machine; Computer science; Quadratic classifier; Machine learning; Artificial intelligence; Software; AdaBoost; Classifier (UML); Linear discriminant analysis; Software quality; Data mining; Margin classifier; Confusion matrix; Random forest; Pattern recognition (psychology); Software development","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002264542,0.000754786,0.00130212,0.002219076,0.0004835198,0.001025234,0.001139828,0.0007934588,0.00117822],"category_scores_gemma":[0.005174315,0.0002333754,0.0006906523,0.001103328,0.0001793168,0.00121271,0.0007559907,0.001021387,0.0009092019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005718196,"about_ca_system_score_gemma":0.0006532752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003440894,"about_ca_topic_score_gemma":0.002444797,"domain_scores_codex":[0.9981376,0.0003467459,0.0002130804,0.0004084553,0.0007271101,0.0001669284],"domain_scores_gemma":[0.9978765,0.0004999403,0.0001958388,0.0001913245,0.001142142,0.00009435385],"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":[0.0004495505,0.0003888135,0.007607152,0.0001125942,0.0001640183,0.0001575972,0.0001168564,0.04107465,0.01667465,0.00133264,0.003740947,0.9281805],"study_design_scores_gemma":[0.00001469819,0.0001995482,0.003185572,0.00001269366,0.00004935063,0.0000609753,0.00003938799,0.9868693,0.00759672,0.0009179887,0.001029587,0.00002414256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1058007,0.0006507929,0.8861465,0.0001676934,0.0002147611,0.0002383244,0.0002550861,0.004848752,0.001677432],"genre_scores_gemma":[0.788048,0.0002641681,0.2086307,0.00008435246,0.00008595065,0.0002811162,0.0006751728,0.00006444175,0.00186621],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003440894,"threshold_uncertainty_score":0.01197618,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03609607299771823,"score_gpt":0.3145799106342511,"score_spread":0.2784838376365328,"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."}}