Paper Abstract and Keywords |
Presentation |
2016-11-16 15:00
Selective Inference for High-Dimensional Binary Classification Yuta Umezu, Kazuya Nakagawa (NIT), Koji Tsuda (Univ. of Tokyo), Ichiro Takeuchi (NIT) IBISML2016-59 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
In machine learning and other related area, the number of features is often reduced by some feature selection procedure as a pre-processing for data analysis when it is large. However, once features are selected from the data via some algorithm, we need to correct a selection bias for a statistical inference after feature selection. A selective inference is a kind of statistical inference for correcting the selection bias, and several results are developed until now. Almost results, however, require the normality of the response, so we can not apply it for other important tasks such as classification. In this paper, we extend the selective inference for classification problem based on the high dimensional asymptotic theory. Through some simulation studies, we check the performance of our proposal. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Family-wise Error Rate / High Dimensional Asymptotics / Hypothesis Testing / Selective Inference / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 116, no. 300, IBISML2016-59, pp. 93-100, Nov. 2016. |
Paper # |
IBISML2016-59 |
Date of Issue |
2016-11-09 (IBISML) |
ISSN |
Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 |
Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) |
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IBISML2016-59 |
Conference Information |
Committee |
IBISML |
Conference Date |
2016-11-16 - 2016-11-18 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Kyoto Univ. |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Information-Based Induction Science Workshop (IBIS2016) |
Paper Information |
Registration To |
IBISML |
Conference Code |
2016-11-IBISML |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Selective Inference for High-Dimensional Binary Classification |
Sub Title (in English) |
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Keyword(1) |
Family-wise Error Rate |
Keyword(2) |
High Dimensional Asymptotics |
Keyword(3) |
Hypothesis Testing |
Keyword(4) |
Selective Inference |
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1st Author's Name |
Yuta Umezu |
1st Author's Affiliation |
Nagoya Institute of Technology (NIT) |
2nd Author's Name |
Kazuya Nakagawa |
2nd Author's Affiliation |
Nagoya Institute of Technology (NIT) |
3rd Author's Name |
Koji Tsuda |
3rd Author's Affiliation |
University of Tokyo (Univ. of Tokyo) |
4th Author's Name |
Ichiro Takeuchi |
4th Author's Affiliation |
Nagoya Institute of Technology (NIT) |
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Speaker |
Author-1 |
Date Time |
2016-11-16 15:00:00 |
Presentation Time |
180 minutes |
Registration for |
IBISML |
Paper # |
IBISML2016-59 |
Volume (vol) |
vol.116 |
Number (no) |
no.300 |
Page |
pp.93-100 |
#Pages |
8 |
Date of Issue |
2016-11-09 (IBISML) |
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