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Lecture Notes in Computer Science
Feature generation is the problem of automatically constructing good features for a given target learning problem. While most feature generation algorithms belong either to the filter or to the wrapper approach, this paper focuses on embedded feature generation. We propose a general scheme to embed feature generation in a wide range of tree-based learning algorithms, including single decision trees, random forests and tree boosting. It is based on the formalization of feature construction as adoi:10.1007/978-3-642-33460-3_18 fatcat:mfwv4fivzncazmh2f4es37jgmi