{"id":"W1471542436","doi":"10.1016/j.artint.2016.04.003","title":"ASlib: A benchmark library for algorithm selection","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":213,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft; Microsoft","keywords":"Computer science; Selection (genetic algorithm); Benchmark (surveying); Exploit; Task (project management); Set (abstract data type); Variety (cybernetics); Data mining; Selection algorithm; Range (aeronautics); Algorithm; Machine learning; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003632026,0.004083215,0.001940365,0.005574837,0.00108087,0.003465727,0.006065022,0.002636231,0.02917494],"category_scores_gemma":[0.01839225,0.001315071,0.002112807,0.008753804,0.0006638374,0.003174958,0.002139326,0.002766719,0.02193213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00135894,"about_ca_system_score_gemma":0.003677071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005246897,"about_ca_topic_score_gemma":0.007185344,"domain_scores_codex":[0.9959049,0.001368114,0.0006313844,0.000468453,0.001215743,0.0004115051],"domain_scores_gemma":[0.9906844,0.005167089,0.000381245,0.001546416,0.001877998,0.0003428987],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001080231,0.0007177115,0.001828908,0.003827329,0.0004664825,0.0002142019,0.0001095981,0.04588515,0.002494574,0.007018102,0.6180547,0.318303],"study_design_scores_gemma":[0.003520534,0.0009133805,0.002826098,0.0009418671,0.0005174764,0.0008820263,0.0002182718,0.5151961,0.03220281,0.03740518,0.4051512,0.0002250927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05930647,0.01963295,0.3810522,0.00249454,0.002415851,0.001975762,0.1136165,0.32293,0.0965757],"genre_scores_gemma":[0.09621303,0.00627061,0.6434432,0.001503926,0.0004366471,0.002881761,0.1890014,0.03720484,0.02304456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02917494,"threshold_uncertainty_score":0.09759986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03557090669313157,"score_gpt":0.2896685730587838,"score_spread":0.2540976663656522,"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."}}