Presentation | 2019-06-17 Adaptive Discretization based Predictive Sequence Mining for Continuous Time Series Yoshikazu Shibahara, Takuto Sakuma, Ichiro Takeuchi, Masayuki Karasuyama, |
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PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In recent years, improvement of sensor performance and spread of portable devices such as smartphones enable us to easily collect time-series data. Thus, it is an important task to extract valuable information from time series-data. In this research, we propose a method extracting a class specific patterns from time-series data by using an adaptive discretization algorithm for a continuous feature space. Conventional approaches need to define a symbolized representation of the original continuous time-series data beforehand. Our approach can construct a sparse linear model by selecting important patterns from a variety of possible symbolizations. The proposed method efficiently deals with a huge number of patterns by combining a safe-screening technique and sequence pattern mining. Our numerical experiments demonstrate effectiveness of our approach through several benchmark datasets. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | Continuous valued sequence data / sparse modeling / sequence mining |
Paper # | IBISML2019-9 |
Date of Issue | 2019-06-10 (IBISML) |
Conference Information | |
Committee | NC / IBISML / IPSJ-MPS / IPSJ-BIO |
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Conference Date | 2019/6/17(3days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | Okinawa Institute of Science and Technology |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | Neurocomputing, Machine Learning Approach to Biodata Mining, and General |
Chair | Hayaru Shouno(UEC) / Hisashi Kashima(Kyoto Univ.) / Masakazu Sekijima(Tokyo Tech) / Hiroyuki Kurata(Kyutech) |
Vice Chair | Kazuyuki Samejima(Tamagawa Univ) / Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo) |
Secretary | Kazuyuki Samejima(NAIST) / Masashi Sugiyama(NTT) / Koji Tsuda(Nagoya Inst. of Tech.) / (AIST) / (Nagoya Univ.) |
Assistant | Takashi Shinozaki(NICT) / Ken Takiyama(TUAT) / Tomoharu Iwata(NTT) / Shigeyuki Oba(Kyoto Univ.) |
Paper Information | |
Registration To | Technical Committee on Neurocomputing / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / IPSJ Special Interest Group on Mathematical Modeling and Problem Solving / IPSJ Special Interest Group on Bioinformatics and Genomics |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Adaptive Discretization based Predictive Sequence Mining for Continuous Time Series |
Sub Title (in English) | |
Keyword(1) | Continuous valued sequence data |
Keyword(2) | sparse modeling |
Keyword(3) | sequence mining |
1st Author's Name | Yoshikazu Shibahara |
1st Author's Affiliation | Nagoya Institute of Technolog(NIT) |
2nd Author's Name | Takuto Sakuma |
2nd Author's Affiliation | Nagoya Institute of Technolog(NIT) |
3rd Author's Name | Ichiro Takeuchi |
3rd Author's Affiliation | Nagoya Institute of Technology/RIKEN Center for Advanced Intelligence Project/National Institute for Materials Science(NIT/RIKEN/NIMS) |
4th Author's Name | Masayuki Karasuyama |
4th Author's Affiliation | Nagoya Institute of Technology/National Institute for Materials Science(NIT/NIMS) |
Date | 2019-06-17 |
Paper # | IBISML2019-9 |
Volume (vol) | vol.119 |
Number (no) | IBISML-89 |
Page | pp.pp.57-64(IBISML), |
#Pages | 8 |
Date of Issue | 2019-06-10 (IBISML) |