{"id":"W2508392542","doi":"10.1109/icws.2016.16","title":"An LDA-SVM Active Learning Framework for Web Service Classification","year":2016,"lang":"en","type":"article","venue":"","topic":"Service-Oriented Architecture and Web Services","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Machine learning; Classifier (UML); Support vector machine; Artificial intelligence; Leverage (statistics); Scalability; Probabilistic logic; Probabilistic classification; Web service; Training set; Data mining; Naive Bayes classifier; World Wide Web; Database","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":[0.002532034,0.001021583,0.001653246,0.001978642,0.0009113967,0.001466709,0.002570911,0.001355901,0.003197561],"category_scores_gemma":[0.003824004,0.0005236122,0.00130197,0.002008948,0.0004329609,0.001758124,0.001265705,0.002239933,0.002034088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035121,"about_ca_system_score_gemma":0.001373393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006276451,"about_ca_topic_score_gemma":0.006954608,"domain_scores_codex":[0.9983832,0.0006306696,0.0001082829,0.0002530272,0.0004968414,0.0001278902],"domain_scores_gemma":[0.9986494,0.0004981603,0.00008656909,0.0001357052,0.0005573313,0.00007281976],"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":[0.0002282435,0.000515815,0.002249873,0.0001512433,0.0001456338,0.00008360994,0.000116688,0.1940608,0.004069035,0.02152651,0.01448192,0.7623707],"study_design_scores_gemma":[0.000005809974,0.00001309688,0.00008430353,0.000003199385,0.000004779879,0.00001188988,0.000006523459,0.9947378,0.0004202227,0.003446478,0.001260593,0.000005182492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002354516,0.0002994722,0.9952829,0.0001712849,0.0000593855,0.00006387584,0.00009592247,0.001051964,0.0006207898],"genre_scores_gemma":[0.2585493,0.0007348434,0.7306445,0.000396194,0.0004485802,0.0008012178,0.001379258,0.0002717498,0.006774422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006276451,"threshold_uncertainty_score":0.01339084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02214543851793423,"score_gpt":0.285516222457192,"score_spread":0.2633707839392578,"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."}}