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Paper Abstract and Keywords
Presentation 2017-09-15 10:00
Quantum-Inspired Regression Forest
Zeke Xie, Issei Sato (UTokyo) PRMU2017-40 IBISML2017-12
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a Quantum-Inspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections. We assign each principal component a Fraction Transition Probability as its probability weight based on Principal Component Analysis and quantum interpretations. In order to generate the feature subset for each base regressor, we select a feature subset from principal components based on Fraction Transition Probabilities. The idea originating from quantum mechanics can encourage ensemble diversity and the accuracy simultaneously. We incorporate Quantum-Inspired Subspace Method into Random Forest and propose Quantum-Inspired Forest. We theoretically prove that the quantum interpretation corresponds to the first order approximation of ensemble regression. We also evaluate the empirical performance of Quantum-Inspired Forest and Random Forest in multiple hyperparameter settings. Quantum-Inspired Forest prove the significant robustness of the default hyperparameters on most data sets. The contribution of this work is two-fold, a novel ensemble regression algorithm inspired by quantum mechanics and the theoretical connection between quantum interpretations and machine learning algorithms.
Keyword (in Japanese) (See Japanese page) 
(in English) Supervised Learning / Ensemble Method / Regression Tree / Feature Selection / Quantum Physics / / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 211, IBISML2017-12, pp. 7-17, Sept. 2017.
Paper # IBISML2017-12 
Date of Issue 2017-09-08 (PRMU, IBISML) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
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)
Download PDF PRMU2017-40 IBISML2017-12

Conference Information
Conference Date 2017-09-15 - 2017-09-16 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IBISML 
Conference Code 2017-09-PRMU-IBISML-CVIM 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Quantum-Inspired Regression Forest 
Sub Title (in English)  
Keyword(1) Supervised Learning  
Keyword(2) Ensemble Method  
Keyword(3) Regression Tree  
Keyword(4) Feature Selection  
Keyword(5) Quantum Physics  
1st Author's Name Zeke Xie  
1st Author's Affiliation The University of Tokyo (UTokyo)
2nd Author's Name Issei Sato  
2nd Author's Affiliation The University of Tokyo (UTokyo)
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Speaker Author-1 
Date Time 2017-09-15 10:00:00 
Presentation Time 30 minutes 
Registration for IBISML 
Paper # PRMU2017-40, IBISML2017-12 
Volume (vol) vol.117 
Number (no) no.210(PRMU), no.211(IBISML) 
Page pp.7-17 
#Pages 11 
Date of Issue 2017-09-08 (PRMU, IBISML) 

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