Classification of sparsely and irregularly sampled time series: A learning in model space approach

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Authors

Colleges, School and Institutes

External organisations

  • University of Exeter

Abstract

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.

Details

Original languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Publication statusPublished - 3 Jul 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period14/05/1719/05/17

ASJC Scopus subject areas