Random Subspace Ensembles of Fully Convolutional Network for Time Series Classification

Yangqianhui Zhang,Liang Zhao,Jiajun Ma,Chunyang Mo

doi:10.3390/app112210957

Yangqianhui Zhang, Liang Zhao + Show 2 more

Open Access

https://doi.org/10.3390/app112210957

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Abstract

Time series classification (TSC) task is one of the most significant topics in data mining. Among all methods for this issue, the deep-learning-based shows superior performance for its good adaption to raw series data and automatic extraction of features. However, rare eyes are kept on composing ensembles of these superior individual classifiers to achieve further breakthroughs. The existing deep learning ensembles NNE did a heavy work of combining 60 individuals but did not maximize the deserving improvement, since it merely pays attention to the diversity of individuals but ignores their accuracy. In this paper, we propose to construct an ensemble of Full Convolutional Neural Networks (FCN) by Random Subspace Method (RSM), named RSM-FCN. FCN is a simple but outstanding individual classifier and RSM is suitable for high dimensional data such as time series, but there are few instances. Thus, the combination of these strengths, RSM-FCN provides a highly cost-effective approach to yield promising results. Experiments on the UCR dataset demonstrate the effectiveness and reasonability of the proposed method.

Highlights

Time series classification (TSC) has attracted great attention, for it exists in a wide variety of fields [1], such as recognition and diagnosis problems in many industries, such as finance and medicine
Methods based on deep learning shows impressive performance, as they have certain tolerance for the unaligned time series and be able to perceive the features benefiting for classification task automatically
Based on the above analysis, we propose to employ the simple-structure but excellent Fully Convolutional Network (FCN) and the classical data perturbation manner Random Subspace Method (RSM) [18] to build a deep learning ensemble

Summary

Introduction

Time series classification (TSC) has attracted great attention, for it exists in a wide variety of fields [1], such as recognition and diagnosis problems in many industries, such as finance and medicine. Based on the above analysis, we propose to employ the simple-structure but excellent FCN and the classical data perturbation manner Random Subspace Method (RSM) [18] to build a deep learning ensemble. In this paper, a lightweight RSE-FCN (Random Subspace Ensembles of FCN) is designed to tackle the challenging TSC problem, where the -divided time series intervals are regarded as candidate subsequences, and Top-K ones with significantly discriminative feature are screened out by evaluation model, with Random Subspace Method deploying on the Top-K and the superior FCN serving as individual classifier, the. This work proposes a Random Subspace Ensemble of FCN (RSE-FCN), combining the strength of above all, in order to yield promising TSC results It converts raw continuous time series into discriminative subsequences, deploys Random Subspace Method on these subsequences with FCN classifier.

Experiments

Conclusions