TY - GEN
T1 - Classification of sparsely and irregularly sampled time series
T2 - 2017 International Joint Conference on Neural Networks, IJCNN 2017
AU - Shen, Yuan
AU - Tino, Peter
AU - Tsaneva-Atanasova, Krasimira
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - Classification of sparsely and irregularly sampled time series data is a challenging machine learning task. To tackle this problem, we present a learning in model space framework in which time-continuous dynamical system models are first inferred from individual time series and then the inferred models are used to represent these time series for the classification task. In contrast to the existing approaches using model point estimates to represent individual time series, we further employ posterior distributions over models, thus taking into account in a principled manner the uncertainty around the inferred model due to observation noise and data sparsity. Finally, we present a distributional classifier for classifying the posterior distributions. We evaluate the framework on a biological pathway model. In particular, we investigate the classification performance in the cases where model uncertainties in the training and test phases do not match.
AB - Classification of sparsely and irregularly sampled time series data is a challenging machine learning task. To tackle this problem, we present a learning in model space framework in which time-continuous dynamical system models are first inferred from individual time series and then the inferred models are used to represent these time series for the classification task. In contrast to the existing approaches using model point estimates to represent individual time series, we further employ posterior distributions over models, thus taking into account in a principled manner the uncertainty around the inferred model due to observation noise and data sparsity. Finally, we present a distributional classifier for classifying the posterior distributions. We evaluate the framework on a biological pathway model. In particular, we investigate the classification performance in the cases where model uncertainties in the training and test phases do not match.
UR - http://www.scopus.com/inward/record.url?scp=85031046300&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2017.7966321
DO - 10.1109/IJCNN.2017.7966321
M3 - Conference contribution
AN - SCOPUS:85031046300
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 3696
EP - 3703
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 May 2017 through 19 May 2017
ER -