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| Content Provider | ACM Digital Library |
|---|---|
| Author | Liang, Guohua Zhang, Chengqi |
| Abstract | Imbalanced time series classification (TSC) involving many real-world applications has increasingly captured attention of researchers. Previous work has proposed an intelligent-structure preserving over-sampling method (SPO), which the authors claimed achieved better performance than other existing over-sampling and state-of-the-art methods in TSC. The main disadvantage of over-sampling methods is that they significantly increase the computational cost of training a classification model due to the addition of new minority class instances to balance data-sets with high dimensional features. These challenging issues have motivated us to find a simple and efficient solution for imbalanced TSC. Statistical tests are applied to validate our conclusions. The experimental results demonstrate that this proposed simple random under-sampling technique with SVM is efficient and can achieve results that compare favorably with the existing complicated SPO method for imbalanced TSC. |
| Starting Page | 2339 |
| Ending Page | 2342 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781450311564 |
| DOI | 10.1145/2396761.2398635 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2012-10-29 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Under-sampling Imbalanced time series classification Svm |
| Content Type | Text |
| Resource Type | Article |
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