Selective Inference for Sparse High-Order Interaction Models
Abstract
Finding statistically significant high-order interactions in predictive modeling is important but challenging task because the possible number of high-order interactions is extremely large (e.g., $> 10^{17}$). In this paper we study feature selection and statistical inference for sparse high-order interaction models. Our main contribution is to extend recently developed selective inference framework for linear models to high-order interaction models by developing a novel algorithm for efficiently characterizing the selection event for the selective inference of high-order interactions. We demonstrate the effectiveness of the proposed algorithm by applying it to an HIV drug response prediction problem.
Cite
Text
Suzumura et al. "Selective Inference for Sparse High-Order Interaction Models." International Conference on Machine Learning, 2017.Markdown
[Suzumura et al. "Selective Inference for Sparse High-Order Interaction Models." International Conference on Machine Learning, 2017.](https://mlanthology.org/icml/2017/suzumura2017icml-selective/)BibTeX
@inproceedings{suzumura2017icml-selective,
title = {{Selective Inference for Sparse High-Order Interaction Models}},
author = {Suzumura, Shinya and Nakagawa, Kazuya and Umezu, Yuta and Tsuda, Koji and Takeuchi, Ichiro},
booktitle = {International Conference on Machine Learning},
year = {2017},
pages = {3338-3347},
volume = {70},
url = {https://mlanthology.org/icml/2017/suzumura2017icml-selective/}
}