TY - GEN
T1 - Interpretable Multivariate Time Series Classification Based on Prototype Learning
AU - Ma, Dengjuan
AU - Wang, Zhu
AU - Xie, Jia
AU - Guo, Bin
AU - Yu, Zhiwen
N1 - Publisher Copyright:
© 2020, Springer Nature Switzerland AG.
PY - 2020
Y1 - 2020
N2 - Recently, the classification of multivariate time series has attracted much attention in the field of machine learning and data mining, due to its wide application values in biomedicine, finance, industry and so on. During the last decade, deep learning has achieved great success in many tasks. However, while many studies have applied deep learning to time series classification, few works can provide good interpretability. In this paper, we propose a deep sequence model with built-in interpretability by fusing deep learning with prototype learning, aiming to achieve interpretable classification of multivariate time series. In particular, an input sequence is classified by being compared with a set of prototypes, which are also sequences learned by the developed model, i.e., exemplary cases in the problem domain. We use the matched subset of the MIMIC-III Waveform Database to evaluate the proposed model and compare it with several baseline models. Experimental results show that our model can not only achieve the best performance but also provide good interpretability.
AB - Recently, the classification of multivariate time series has attracted much attention in the field of machine learning and data mining, due to its wide application values in biomedicine, finance, industry and so on. During the last decade, deep learning has achieved great success in many tasks. However, while many studies have applied deep learning to time series classification, few works can provide good interpretability. In this paper, we propose a deep sequence model with built-in interpretability by fusing deep learning with prototype learning, aiming to achieve interpretable classification of multivariate time series. In particular, an input sequence is classified by being compared with a set of prototypes, which are also sequences learned by the developed model, i.e., exemplary cases in the problem domain. We use the matched subset of the MIMIC-III Waveform Database to evaluate the proposed model and compare it with several baseline models. Experimental results show that our model can not only achieve the best performance but also provide good interpretability.
KW - Deep learning
KW - Interpretable classification
KW - Multivariate time series
KW - Prototype
UR - http://www.scopus.com/inward/record.url?scp=85097828583&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-64243-3_16
DO - 10.1007/978-3-030-64243-3_16
M3 - 会议稿件
AN - SCOPUS:85097828583
SN - 9783030642426
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 205
EP - 216
BT - Green, Pervasive, and Cloud Computing - 15th International Conference, GPC 2020, Proceedings
A2 - Yu, Zhiwen
A2 - Becker, Christian
A2 - Xing, Guoliang
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference on Green, Pervasive, and Cloud Computing, GPC 2020
Y2 - 13 November 2020 through 15 November 2020
ER -