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Selective Inference for Sparse High-Order Interaction Models
Shinya Suzumura · Kazuya Nakagawa · Yuta Umezu · Koji Tsuda · Ichiro Takeuchi

Mon Aug 07 11:24 PM -- 11:42 PM (PDT) @ C4.4
in Sparsity 2 »
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.
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Author Information

Shinya Suzumura (Nagoya Institute of Technology)
Kazuya Nakagawa (Nagoya Institute of Technology)
Yuta Umezu (Nagoya Institute of Technology)
Koji Tsuda (University of Tokyo / RIKEN)
Ichiro Takeuchi (Nagoya Institute of Technology / RIKEN)

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