An Effective Confidence-Based Early Classification of Time Series

Junwei Lv,Peipei Li,Lei Li,Xuegang Hu

doi:10.1109/access.2019.2929644

Abstract

Early classification of time series aims to predict the class value of a sequence accurately as early as possible, not wait for the full-length data, which is significant in many time-sensitive applications and has attracted great interest in recent years. For instance, early diagnosis can help patients get early treatment and even save their lives. The problem of early classification is how to determine whether the collected data are sufficient to output the class value. Moreover, in practical applications, users also need to know the confidence (reliability) of the prediction results for more appropriate processing. For example, giving a healthy patient the possibility of suffering from some disease can assist physicians in an optimal therapy. However, existing work has not provided an effective measure to indicate how accurate the classification is. Therefore, in this paper, we propose an effective confidence-based early classification of time series. Firstly, based on a set of base time series classifiers trained at different timestamps, we propose a dynamic decision fusion method to measure the confidence of a predicted result by fusing the results of multiple base classifiers. Secondly, by analyzing the distribution of confidence values, we develop an adaptive learning method for the confidence threshold to simultaneously optimize the two conflicting objectives: accuracy and earliness. Finally, the experimental results conducted on 45 equal-length datasets and 8 variable-length datasets clearly show that our proposed approach can achieve the superior in early classification compared to state-of-the-art approaches.

Highlights

Time series classification (TSC) has attracted a significant interest within many fields, due to the fact that time series data are present in a wide range of real-life domains including, but not limited to, biology [1], medicine [2], traffic [3], and engineering [4]
Based on a set of base time series classifiers trained at different timestamps, we propose a dynamic decision fusion method to measure the confidence of a predicted result by fusing the results of multiple base classifiers
In order to respect the achievements of original authors, we use the same results as those published results

Summary

Introduction

Time series classification (TSC) has attracted a significant interest within many fields, due to the fact that time series data are present in a wide range of real-life domains including, but not limited to, biology [1], medicine [2], traffic [3], and engineering [4]. Time series data are collected over time. Classical time series classification is to construct a classifier from training time series samples that can correctly predict the class labels of new unlabeled samples after they are fully collected. Classification of time series means to classify time series data as early as possible while maintaining a high level. In human-computer interaction, people’s intentions can be predicted by early recognition of human actions or gestures captured by sensors or cameras, which can greatly shorten the response time of the system and provide more natural communication experience [6]. Chemical leaks can be identified as early as possible to avoid more serious damage, by using odor signals obtained from a set of sensors [7]

Methods

Results

Conclusion