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Generating custom classification datasets by targeting the instance space.

, and . GECCO (Companion), page 1582-1588. ACM, (2017)

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Non-parametric model of the space of continuous black-box optimization problems., and . GECCO (Companion), page 175-176. ACM, (2017)Towards an Improved Understanding of Features for More Interpretable Landscape Analysis., and . GECCO Companion, page 135-138. ACM, (2024)A Meta-learning Prediction Model of Algorithm Performance for Continuous Optimization Problems., , and . PPSN (1), volume 7491 of Lecture Notes in Computer Science, page 226-235. Springer, (2012)On normalization and algorithm selection for unsupervised outlier detection., , , and . Data Min. Knowl. Discov., 34 (2): 309-354 (2020)Instance Space Analysis for Unsupervised Outlier Detection., , and . EDML@SDM, volume 2436 of CEUR Workshop Proceedings, page 32-41. CEUR-WS.org, (2019)ICARUS: Identification of complementary algorithms by uncovered sets., and . CEC, page 2427-2432. IEEE, (2016)Extending Instance Space Analysis to Algorithm Configuration Spaces., , , , and . GECCO Companion, page 147-150. ACM, (2024)Simplifying the Bacteria Foraging Optimization Algorithm., , , and . IEEE Congress on Evolutionary Computation, page 1-7. IEEE, (2010)Instance spaces for machine learning classification, , , and . Machine Learning, 107 (1): 109--147 (2018)Landscape characterization of numerical optimization problems using biased scattered data., , and . IEEE Congress on Evolutionary Computation, page 1-8. IEEE, (2012)