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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods
verfasst von : Francis Maes, Pierre Geurts, Louis Wehenkel
Erschienen in: Machine Learning and Knowledge Discovery in Databases
Verlag: Springer Berlin Heidelberg
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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 a sequential decision making problem addressed by a tractable Monte Carlo search algorithm coupled with node splitting. This leads to fast algorithms that are applicable to large-scale problems. We empirically analyze the performances of these tree-based learners combined or not with the feature generation capability on several standard datasets.