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  "Title": "Sparse Wrapper Algorithm",
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  "Description": "An algorithm that trains a meta-learning procedure that\ncombines screening and wrapper methods to find a set of\nextremely low-dimensional attribute combinations. This package\nworks on top of the 'caret' package and proceeds in a\nforward-step manner. More specifically, it builds and tests\nlearners starting from very few attributes until it includes a\nmaximal number of attributes by increasing the number of\nattributes at each step. Hence, for each fixed number of\nattributes, the algorithm tests various (randomly selected)\nlearners and picks those with the best performance in terms of\ntraining error. Throughout, the algorithm uses the information\ncoming from the best learners at the previous step to build and\ntest learners in the following step. In the end, it outputs a\nset of strong low-dimensional learners.",
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  "Date/Publication": "2023-08-22 08:14:39 UTC",
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