Title:
CAUSAL RELATION LEARNING DEVICE, CAUSAL RELATION ESTIMATING DEVICE, CAUSAL RELATION LEARNING METHOD, CAUSAL RELATION ESTIMATING METHOD, AND PROGRAM
Document Type and Number:
WIPO Patent Application WO/2019/194105
Kind Code:
A1
Abstract:
Disclosed is a feature for estimating a causal relation with which it is possible to solve problems due to prior art, and which does not require preliminary settings of a regression model. One embodiment of the present invention pertains to a causal relation learning device having: a feature quantity calculation unit for accepting as inputs a correct answer label, which is a classification label that pertains to a causal relation of time-series data and is grouped into three or more classes, and time-series data that corresponds to the correct answer label, and calculating a feature quantity of the time-series data; and a classifier learning unit for learning a classifier using a set of the feature quantity and the correct answer label so that the output of a classifier for the feature quantity equals the maximum value of output value of the correct answer label.
Inventors:
CHIKAHARA YOICHI (JP)
FUJINO AKINORI (JP)
FUJINO AKINORI (JP)
Application Number:
PCT/JP2019/014236
Publication Date:
October 10, 2019
Filing Date:
March 29, 2019
Export Citation:
Assignee:
NIPPON TELEGRAPH & TELEPHONE (JP)
International Classes:
G06N20/00
Other References:
CHIKAHARA, Y. ET AL.: "A Supervised Learning Approach to Causal Inference for Bivariate Time Series", IEICE TECHNICAL REPORT, vol. 116, no. 121, 27 June 2016 (2016-06-27), pages 189 - 194, ISSN: 0913-5685
LOPEZ-PAZ, D. ET AL.: "Towards a Learning Theory of Cause-Effect Inference", PROCEEDINGS OF MACHINE LEARNING RESEARCH, vol. 37, 9 July 2015 (2015-07-09), pages 1452 - 1461, XP055643081, ISSN: 2640-3498, Retrieved from the Internet [retrieved on 20190425]
SUN, X.: "Assessing Nonlinear Granger Causality from Multivariate Time Series", PROCEEDINGS OF ECML PKDD 2008, vol. 5212, 2008, pages 440 - 455, XP019105903, ISBN: 978-3-540-87480-5, Retrieved from the Internet [retrieved on 20190425]
ZHU, P. ET AL.: "Learning Nonlinear Generative Models of Time Series With a Kalman Filter in RKHS", IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 62, 27 September 2013 (2013-09-27), pages 141 - 155, XP011533602, ISSN: 1053-587X, DOI: 10.1109/TSP.2013.2283842
LOPEZ-PAZ, D. ET AL.: "Towards a Learning Theory of Cause-Effect Inference", PROCEEDINGS OF MACHINE LEARNING RESEARCH, vol. 37, 9 July 2015 (2015-07-09), pages 1452 - 1461, XP055643081, ISSN: 2640-3498, Retrieved from the Internet
SUN, X.: "Assessing Nonlinear Granger Causality from Multivariate Time Series", PROCEEDINGS OF ECML PKDD 2008, vol. 5212, 2008, pages 440 - 455, XP019105903, ISBN: 978-3-540-87480-5, Retrieved from the Internet
ZHU, P. ET AL.: "Learning Nonlinear Generative Models of Time Series With a Kalman Filter in RKHS", IEEE TRANSACTIONS ON SIGNAL PROCESSING, vol. 62, 27 September 2013 (2013-09-27), pages 141 - 155, XP011533602, ISSN: 1053-587X, DOI: 10.1109/TSP.2013.2283842
Attorney, Agent or Firm:
ITOH, Tadashige et al. (JP)
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