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Towards understanding the importance of time-series features in automated algorithm performance prediction., , and . Expert Syst. Appl., 213 (Part): 119023 (2023)BuTTER: BidirecTional LSTM for Food Named-Entity Recognition., , , , and . IEEE BigData, page 3550-3556. IEEE, (2020)FooDis: A food-disease relation mining pipeline., , and . Artif. Intell. Medicine, (August 2023)Improving Nevergrad's Algorithm Selection Wizard NGOpt Through Automated Algorithm Configuration., , , , , , , , and . PPSN (1), volume 13398 of Lecture Notes in Computer Science, page 18-31. Springer, (2022)Identifying minimal set of Exploratory Landscape Analysis features for reliable algorithm performance prediction., , , , and . CEC, page 1-8. IEEE, (2022)SciFoodNER: Food Named Entity Recognition for Scientific Text., , , and . IEEE Big Data, page 4065-4073. IEEE, (2022)Generalization Ability of Feature-based Performance Prediction Models: A Statistical Analysis across Benchmarks., , , , and . CoRR, (2024)CafeteriaSA corpus: scientific abstracts annotated across different food semantic resources., , , , , , , , and . Database J. Biol. Databases Curation, (2022)SELECTOR: selecting a representative benchmark suite for reproducible statistical comparison., , , , , and . GECCO, page 620-629. ACM, (2022)How Far Out of Distribution Can We Go With ELA Features and Still Be Able to Rank Algorithms?, and . SSCI, page 341-346. IEEE, (2023)