| 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) |