{"id":"W2908387075","doi":"10.1109/icebe.2018.00014","title":"Meta-Feature Based Data Mining Service Selection and Recommendation Using Machine Learning Models","year":2018,"lang":"en","type":"article","venue":"","topic":"Data Mining Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Support vector machine; Machine learning; Artificial intelligence; Feature selection; Data mining; Quality of service; Meta learning (computer science); Multilayer perceptron; Web service; Process (computing); Service (business); Perceptron; Selection (genetic algorithm); Artificial neural network; World Wide Web; Engineering","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":[0.004225891,0.001361577,0.002837323,0.003688561,0.0006634906,0.002070246,0.002161507,0.001407647,0.0007134455],"category_scores_gemma":[0.009597234,0.0006037769,0.002760101,0.00331847,0.0003878156,0.00228999,0.0006906922,0.00163859,0.0004501641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00171478,"about_ca_system_score_gemma":0.001439955,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008971529,"about_ca_topic_score_gemma":0.00977183,"domain_scores_codex":[0.9974661,0.0008193323,0.0003440045,0.00053641,0.0006248477,0.0002093736],"domain_scores_gemma":[0.9925039,0.004346035,0.000697816,0.0006323902,0.001609049,0.0002107958],"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":[0.0004727798,0.0007828903,0.02631682,0.0003107193,0.0008950486,0.0002814679,0.000158773,0.7175446,0.004357601,0.002906037,0.002303598,0.2436696],"study_design_scores_gemma":[0.000007622075,0.00004900335,0.0007324471,0.00001029893,0.00003171961,0.00002863805,0.00001472154,0.9968105,0.0009353473,0.001152094,0.0002187121,0.000008791759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1442556,0.001596307,0.8479936,0.001113496,0.0001241727,0.0002772274,0.0007922065,0.002401706,0.001445587],"genre_scores_gemma":[0.7759159,0.0004250754,0.2210805,0.0002144964,0.00009100766,0.0002282236,0.00111695,0.00006474924,0.0008630038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008971529,"threshold_uncertainty_score":0.02234894,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2351521537479223,"score_gpt":0.3235037021648072,"score_spread":0.08835154841688489,"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."}}