Efficient Shapelet Discovery for Time Series Classification
| Autor: | Byron Choi, Guozhong Li, Sourav S. Bhowmick, Kwok-Pan Chun, Grace Lh Wong, Jianliang Xu |
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| Rok vydání: | 2022 |
| Předmět: |
Time series classification
accuracy business.industry Computer science Computation Pattern recognition 02 engineering and technology Bloom filter Class (biology) shapelet discovery Computer Science Applications Set (abstract data type) Computational Theory and Mathematics Discriminative model efficiency 020204 information systems Sliding window protocol 0202 electrical engineering electronic engineering information engineering Artificial intelligence business Model building Information Systems |
| Zdroj: | IEEE Transactions on Knowledge and Data Engineering. 34:1149-1163 |
| ISSN: | 2326-3865 1041-4347 1558-2191 |
| DOI: | 10.1109/tkde.2020.2995870 |
| Popis: | Time-series shapelets are discriminative subsequences, recently found effective for time series classification (tsc). It is evident that the quality of shapelets is crucial to the accuracy of tsc. However, major research has focused on building accurate models from some shapelet candidates. To determine such candidates, existing studies are surprisingly simple, e.g., enumerating subsequences of some fixed lengths, or randomly selecting some subsequences as shapelet candidates. The major bulk of computation is then on building the model from the candidates. In this paper, we propose a novel efficient shapelet discovery method, called bspcover, to discover a set of high-quality shapelet candidates for model building. Specifically, bspcover generates abundant candidates via Symbolic Aggregate approXimation with sliding window, then prunes identical and highly similar candidates via Bloom filters, and similarity matching, respectively. We next propose a $p$p-Cover algorithm to efficiently determine discriminative shapelet candidates that maximally represent each time-series class. Finally, any existing shapelet learning method can be adopted to build a classification model. We have conducted extensive experiments with well-known time-series datasets and representative state-of-the-art methods. Results show that bspcover speeds up the state-of-the-art methods by more than 70 times, and the accuracy is often comparable to or higher than existing works. |
| Databáze: | OpenAIRE |
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