Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/164678
Title: A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification
Authors: Zhang, Xiaocai
Peng, Hui
Zhang, Jianjia
Wang, Yang
Keywords: Science::Biological sciences
Issue Date: 2023
Source: Zhang, X., Peng, H., Zhang, J. & Wang, Y. (2023). A cost-sensitive attention temporal convolutional network based on adaptive top-k differential evolution for imbalanced time-series classification. Expert Systems With Applications, 213, 119073-. https://dx.doi.org/10.1016/j.eswa.2022.119073
Journal: Expert Systems with Applications
Abstract: Imbalanced time-series classification (ITSC) is ubiquitous in many real-world applications. In this study, a novel cost-sensitive deep learning framework, namely ACS-ATCN, is proposed for ITSC. With the framework of ACS-ATCN, first, weighted class costs are optimized jointly with the hyperparameters of an attention temporal convolutional network (ATCN). Second, an improved evolutionary algorithm, termed adaptive top-k differential evolution (ATDE), is presented for optimizing class costs as well as the network's hyperparameter. Experiments on five data sets demonstrate that ACS-ATCN achieves a higher average G-mean than other cost-sensitive learning and oversampling algorithms while using much less computational time. Comparison between different deep learning frameworks also confirms its advantages over other existing benchmarking methods in ITSC. Experimental results also reveal that ATDE provides more accurate classification than the vanilla DE algorithm, and saves as high as 41.53% of average computational expense for convergence.
URI: https://hdl.handle.net/10356/164678
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2022.119073
Schools: School of Biological Sciences 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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