The last decade has witnessed a surge of interest in applying deep learning models for discovering sequential patterns from a large volume of data. Recent works show that deep learning models can be further improved by enforcing models to learn a smooth output distribution around each data point. This can be achieved by augmenting training data with slight perturbations that are designed to alter model outputs. Such adversarial training approaches have shown much success in improving the generalization performance of deep learning models on static data, e.g., transaction data or image data captured on a single snapshot. However, when applied to sequential data, the standard adversarial training approaches cannot fully capture the discriminative structure of a sequence. This is because real-world sequential data are often collected over a long period of time and may include much irrelevant information to the classification task. To this end, we develop a novel adversarial training approach for sequential data classification by investigating when and how to perturb a sequence for an effective data augmentation. Finally, we demonstrate the superiority of the proposed method over baselines in a diversity of real-world sequential datasets.
|Original language||English (US)|
|Title of host publication||KDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Publisher||Association for Computing Machinery|
|Number of pages||9|
|State||Published - Jul 25 2019|
|Event||25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States|
Duration: Aug 4 2019 → Aug 8 2019
|Name||Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Conference||25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019|
|Period||8/4/19 → 8/8/19|
Bibliographical noteFunding Information:
This work was funded by the NSF award 1838159.
- Adversarial training
- Attention mechanism
- Sequential data