Research Area:  Machine Learning

Abstract:

Multivariate time series (MTS) classification is a challenging and important task in various domains and real-world applications. Much of prior work on MTS can be roughly divided into neural network (NN)- and pattern-based methods. The former can lead to robust classification performance, but many of the generated patterns are challenging to interpret; while the latter often produce interpretable patterns that may not be helpful for the classification task. In this work, we propose a reinforcement learning (RL) informed PAttern Mining framework (RLPAM) to identify interpretable yet important patterns for MTS classification. Our framework has been validated by 30 benchmark datasets as well as real-world large-scale electronic health records (EHRs) for an extremely challenging task: sepsis shock early prediction. We show that RLPAM outperforms the state-of-the-art NN-based methods on 14 out of 30 datasets as well as on the EHRs. Finally, we show how RL informed patterns can be interpretable and can improve our understanding of septic shock progression.

Keywords:  
Classification
Deep Reinforcement Learning
Time-series
Data Streams
Health
Medicine

Author(s) Name:  Ge Gao , Qitong Gao , Xi Yang , Miroslav Pajic and Min Chi

Publisher name:  IJCAI

DOI:  10.24963/ijcai.2022/415

Volume Information:  

Paper Link:   https://www.ijcai.org/proceedings/2022/415