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Boundary-Focused Generative Adversarial Networks for Imbalanced and Multimodal Time Series

Authors
Lee, Han KyuLee, JiyoonKim, Seoung Bum
Issue Date
1-9월-2022
Publisher
IEEE COMPUTER SOC
Keywords
Time series analysis; Generative adversarial networks; Generators; Training; Correlation; Classification algorithms; Training data; Generative adversarial network; generative model; imbalanced class; oversampling; multimodality
Citation
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, v.34, no.9, pp.4102 - 4118
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume
34
Number
9
Start Page
4102
End Page
4118
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/145769
DOI
10.1109/TKDE.2022.3182327
ISSN
1041-4347
Abstract
Class imbalance problems have been reported as a major issue in various applications. Classification becomes further complicated when an imbalance occurs in time series data sets. To address time series data, it is necessary to consider their characteristics (i.e., high dimensionality, high correlations, and multimodality). Oversampling is a well-known approach for addressing this problem; however, such an approach does not appropriately consider the characteristics of time series data. This paper addresses these limitations by presenting a model-based oversampling approach, a boundary-focused generative adversarial network (BFGAN). The proposed BFGAN employs a specifically designed additional label for reflecting the importance of a sample's position in data space. Furthermore, the BFGAN generates artificial samples after taking into consideration a sample's multimodality and importance by using a suitable modified GAN structure. We present empirical results that reveal a significant improvement in the quality of the generated data when the proposed BFGAN is used as an oversampling algorithm for an imbalanced multimodal time series data set.
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