| Presentation | 2021-06-28 More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method Kazuya Sugiyama, Vo Nguyen Le Duy, Ichiro Takeuchi, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Conditional selective inference (SI) has been actively studied as a new statistical inference framework for data-driven hypotheses. The basic idea of conditional SI is to make inferences conditional on the selection event characterized by a set of linear and/or quadratic inequalities. Conditional SI has been mainly studied in the context of feature selection such as stepwise feature selection (SFS). The main limitation of the existing conditional SI methods is the loss of power due to over-conditioning, which is required for computational tractability. In this study, we develop a more powerful and general conditional SI method for SFS using the homotopy method which enables us to overcome this limitation. We conduct several experiments to demonstrate the effectiveness and efficiency of the proposed method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | conditional SI / data-driven hypothesis / stepwise feature selection / homotopy continuation / statistical power |
| Paper # | NC2021-8,IBISML2021-8 |
| Date of Issue | 2021-06-21 (NC, IBISML) |
| Conference Information | |
| Committee | NC / IBISML / IPSJ-BIO / IPSJ-MPS |
|---|---|
| Conference Date | 2021/6/28(3days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Rieko Osu(Waseda Univ.) / Ichiro Takeuchi(Nagoya Inst. of Tech.) / 倉田 博之(九工大) / 関嶋 政和(東工大) |
| Vice Chair | Hiroshi Yamakawa(Univ of Tokyo) / Masashi Sugiyama(Univ. of Tokyo) |
| Secretary | Hiroshi Yamakawa(ATR) / Masashi Sugiyama(NICT) / (Univ. of Tokyo) / (AIST) |
| Assistant | Nobuhiko Wagatsuma(Toho Univ.) / Tomoki Kurikawa(KMU) / Tomoharu Iwata(NTT) / Atsuyoshi Nakamura(Hokkaido Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Neurocomputing / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Bioinformatics and Genomics / Special Interest Group on Mathematical Modeling and Problem Solving |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | More Powerful and General Selective Inference for Stepwise Feature Selection using Homotopy Method |
| Sub Title (in English) | |
| Keyword(1) | conditional SI |
| Keyword(2) | data-driven hypothesis |
| Keyword(3) | stepwise feature selection |
| Keyword(4) | homotopy continuation |
| Keyword(5) | statistical power |
| 1st Author's Name | Kazuya Sugiyama |
| 1st Author's Affiliation | Nagoya Institute of Technology(Nitech) |
| 2nd Author's Name | Vo Nguyen Le Duy |
| 2nd Author's Affiliation | Nagoya Institute of Technology/RIKEN(Nitech/RIKEN) |
| 3rd Author's Name | Ichiro Takeuchi |
| 3rd Author's Affiliation | Nagoya Institute of Technology/RIKEN(Nitech/RIKEN) |
| Date | 2021-06-28 |
| Paper # | NC2021-8,IBISML2021-8 |
| Volume (vol) | vol.121 |
| Number (no) | NC-79,IBISML-80 |
| Page | pp.pp.55-61(NC), pp.55-61(IBISML), |
| #Pages | 7 |
| Date of Issue | 2021-06-21 (NC, IBISML) |