Summary of Model Selection with a Shapelet-based Distance Measure For Multi-source Transfer Learning in Time Series Classification, by Jiseok Lee and Brian Kenji Iwana
Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification
by Jiseok Lee, Brian Kenji Iwana
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel transfer learning approach for time series classification, which combines multiple source datasets and selects them based on shapelet discovery for effective pre-training. By leveraging this method, the authors show that it is possible to increase the performance of temporal convolutional neural networks (CNN) on time series datasets. The proposed method measures the transferability of datasets using a simple computation, making it efficient for repeated use across different architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary For time series classification, transfer learning can be used to alleviate the need for extensive data training. However, selecting the right source dataset is crucial. This paper introduces a new approach that combines multiple source datasets and selects them based on shapelet discovery. The method shows promise in improving the performance of temporal convolutional neural networks (CNN) on time series datasets. |
Keywords
» Artificial intelligence » Classification » Cnn » Time series » Transfer learning » Transferability
