ICML Conference 2017 Conference Paper
Selective Inference for Sparse High-Order Interaction Models
- Shinya Suzumura
- Kazuya Nakagawa
- Yuta Umezu
- Koji Tsuda
- Ichiro Takeuchi
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.