Title | ||
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T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification. |
Abstract | ||
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Time series classification is a popular and important topic in machine learning, and it suffers from the class imbalance problem in many real-world applications. In this paper, to address the class imbalance problem, we propose a novel and practical oversampling method named T-SMOTE, which can make full use of the temporal information of time-series data. In particular, for each sample of minority class, T-SMOTE generates multiple samples that are close to class border. Then, based on those samples near class border, T-SMOTE synthesizes more samples. Finally, a weighted sampling method is called on both generated samples near class border and synthetic samples. Extensive experiments on a diverse set of both univariate and multivariate time-series datasets demonstrate that T-SMOTE consistently outperforms the current state-of-the-art methods on imbalanced time series classification. More encouragingly, our empirical evaluations show that T-SMOTE performs better in the scenario of early prediction, an important application scenario in industry, which indicates that T-SMOTE could bring benefits in practice. |
Year | DOI | Venue |
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2022 | 10.24963/ijcai.2022/334 | European Conference on Artificial Intelligence |
Keywords | DocType | Citations |
Data Mining: Class Imbalance and Unequal Cost,Data Mining: Mining Spatial and/or Temporal Data | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Pu Zhao | 1 | 32 | 11.73 |
Chuan Luo | 2 | 496 | 41.38 |
Bo Qiao | 3 | 33 | 9.09 |
Lu Wang | 4 | 0 | 0.34 |
Saravan Rajmohan | 5 | 0 | 1.69 |
Qingwei Lin | 6 | 285 | 27.76 |
Dongmei Zhang | 7 | 1439 | 132.94 |