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arXiv · 2004.06373

Minority Oversampling for Imbalanced Time Series Classification

Tuanfei Zhu·Cheng Luo·Jing Li·Siqi Ren·Zhihong Zhang
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Abstract

Many important real-world applications involve time-series data with skewed distribution. Compared to conventional imbalance learning problems, the classification of imbalanced time-series data is more challenging due to high dimensionality and high inter-variable correlation. This paper proposes a structure preserving Oversampling method to combat the High-dimensional Imbalanced Time-series classification (OHIT). OHIT first leverages a density-ratio based shared nearest neighbor clustering algorithm to capture the modes of minority class in high-dimensional space. It then for each mode applies the shrinkage technique of large-dimensional covariance matrix to obtain accurate and reliable covariance structure. Finally, OHIT generates the structure-preserving synthetic samples based on multivariate Gaussian distribution by using the estimated covariance matrices. Experimental results on several publicly available time-series datasets (including unimodal and multimodal) demonstrate the superiority of OHIT against the state-of-the-art oversampling algorithms in terms of F1, G-mean, and AUC.

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Tuanfei Zhu, Cheng Luo, Jing Li, Siqi Ren, Zhihong Zhang. 2020-04-14. Minority Oversampling for Imbalanced Time Series Classification. https://doi.org/10.1016/j.knosys.2022.108764

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