Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods
Abstract
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.
Cite
Text
Maes et al. "Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012. doi:10.1007/978-3-642-33460-3_18Markdown
[Maes et al. "Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2012.](https://mlanthology.org/ecmlpkdd/2012/maes2012ecmlpkdd-embedding/) doi:10.1007/978-3-642-33460-3_18BibTeX
@inproceedings{maes2012ecmlpkdd-embedding,
title = {{Embedding Monte Carlo Search of Features in Tree-Based Ensemble Methods}},
author = {Maes, Francis and Geurts, Pierre and Wehenkel, Louis},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2012},
pages = {191-206},
doi = {10.1007/978-3-642-33460-3_18},
url = {https://mlanthology.org/ecmlpkdd/2012/maes2012ecmlpkdd-embedding/}
}