Please use this identifier to cite or link to this item:
Title: Time series classification using diversified Ensemble Deep Random Vector Functional Link and Resnet features
Authors: Cheng, Wenxin
Suganthan, Ponnuthurai Nagaratnam
Katuwal, Rakesh
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Cheng, W., Suganthan, P. N. & Katuwal, R. (2021). Time series classification using diversified Ensemble Deep Random Vector Functional Link and Resnet features. Applied Soft Computing, 112, 107826-.
Journal: Applied Soft Computing
Abstract: Random Vector Functional Link (RVFL) is popular among researchers in many areas of machine learning. RVFL is preferred by many researchers as RVFL can produce good performance with relatively little training time. Recent works extend RVFL into deep and ensemble versions. However, RVFL does not have effective feature extraction methods commonly used in time series classification. This results in poor performance of RVFL in time series classification tasks. Also, deep RVFL is a relatively new and evolving area of research. In this paper, we present a framework that extracts features from Residual Networks (Resnet) and trains Ensemble Deep Random Vector Functional Link (edRVFL). We use features extracted from every residual block to train an ensemble of edRVFLs. We propose the following enhancements to edRVFL. Firstly, we diversity the structure of edRVFL and the direct link features to encourage diversity. Secondly, we built an ensemble of edRVFLs with the top two activation functions. Thirdly, we use two-stage tuning to save computational costs. Lastly, we perform a weighted average of all decisions made by every edRVFL. Experiments on the 55 largest UCR datasets show that using features extracted from all Residual blocks improves performance. All our proposed enhancements help improve classification accuracy or computational effort. Consequently, our proposed framework outperforms all traditional and deep learning-based time series classification methods.
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2021.107826
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 Elsevier B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

Citations 20

Updated on Jun 5, 2023

Web of ScienceTM
Citations 20

Updated on May 30, 2023

Page view(s)

Updated on Jun 6, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.