Selective Inference for Sparse High-Order Interaction Models

Shinya Suzumura, Kazuya Nakagawa, Yuta Umezu, Koji Tsuda, Ichiro Takeuchi
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:3338-3347, 2017.

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 this Paper

BibTeX
@InProceedings{pmlr-v70-suzumura17a, title = {Selective Inference for Sparse High-Order Interaction Models}, author = {Shinya Suzumura and Kazuya Nakagawa and Yuta Umezu and Koji Tsuda and Ichiro Takeuchi}, booktitle = {Proceedings of the 34th International Conference on Machine Learning}, pages = {3338--3347}, year = {2017}, editor = {Precup, Doina and Teh, Yee Whye}, volume = {70}, series = {Proceedings of Machine Learning Research}, month = {06--11 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v70/suzumura17a/suzumura17a.pdf}, url = {https://proceedings.mlr.press/v70/suzumura17a.html}, 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.} }
Endnote
%0 Conference Paper %T Selective Inference for Sparse High-Order Interaction Models %A Shinya Suzumura %A Kazuya Nakagawa %A Yuta Umezu %A Koji Tsuda %A Ichiro Takeuchi %B Proceedings of the 34th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2017 %E Doina Precup %E Yee Whye Teh %F pmlr-v70-suzumura17a %I PMLR %P 3338--3347 %U https://proceedings.mlr.press/v70/suzumura17a.html %V 70 %X 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.
APA
Suzumura, S., Nakagawa, K., Umezu, Y., Tsuda, K. & Takeuchi, I.. (2017). Selective Inference for Sparse High-Order Interaction Models. Proceedings of the 34th International Conference on Machine Learning, in Proceedings of Machine Learning Research 70:3338-3347 Available from https://proceedings.mlr.press/v70/suzumura17a.html.

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