Random Forests with Random Projections of the Output Space for High Dimensional Multi-Label Classification

Arnaud Joly, Pierre Geurts, Louis Wehenkel

ECML-PKDD 2014 pp. 607-622

doi:10.1007/978-3-662-44848-9_39 /ecmlpkdd/2014/joly2014ecmlpkdd-random/

Abstract

We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification. We show how learning time complexity can be reduced without affecting computational complexity and accuracy of predictions. We also show that random output space projections may be used in order to reach different bias-variance tradeoffs, over a broad panel of benchmark problems, and that this may lead to improved accuracy while reducing significantly the computational burden of the learning stage.

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Cite

Text

Joly et al. "Random Forests with Random Projections of the Output Space for High Dimensional Multi-Label Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014. doi:10.1007/978-3-662-44848-9_39

Markdown

[Joly et al. "Random Forests with Random Projections of the Output Space for High Dimensional Multi-Label Classification." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2014.](https://mlanthology.org/ecmlpkdd/2014/joly2014ecmlpkdd-random/) doi:10.1007/978-3-662-44848-9_39

BibTeX

@inproceedings{joly2014ecmlpkdd-random,
  title     = {{Random Forests with Random Projections of the Output Space for High Dimensional Multi-Label Classification}},
  author    = {Joly, Arnaud and Geurts, Pierre and Wehenkel, Louis},
  booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
  year      = {2014},
  pages     = {607-622},
  doi       = {10.1007/978-3-662-44848-9_39},
  url       = {https://mlanthology.org/ecmlpkdd/2014/joly2014ecmlpkdd-random/}
}