Fuzzy Shared Representation Learning for Multistream Classification
- Publisher:
- Institute of Electrical and Electronics Engineers (IEEE)
- Publication Type:
- Journal Article
- Citation:
- IEEE Transactions on Fuzzy Systems, 2024, PP, (99), pp. 1-13
- Issue Date:
- 2024-01-01
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| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Fuzzy Shared Representation Learning for Multistream Classification.pdf | Accepted version | 6.94 MB |
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Multistream classification aims to predict the target stream by transferring knowledge from labeled source streams amid non-stationary processes with concept drifts. While existing methods address label scarcity, covariate shift and asynchronous concept drift, they focus solely on the original feature space, neglecting the influence of redundant or low-quality features with uncertainties. Therefore, the advancement of this task is still challenged by how to: 1) ensure guaranteed joint representations of different streams, 2) grapple with uncertainty and interpretability during knowledge transfer, and 3) track and adapt the asynchronous drifts in each stream. To address these challenges, we propose an interpretable Fuzzy Shared Representation Learning (FSRL) method based on the Takagi-SugenoKang (TSK) fuzzy system. Specifically, FSRL accomplishes the non-linear transformation of individual streams by learning the fuzzy mapping with the antecedents of the TSK fuzzy system, there by effectively preserving discriminative information for each original stream in an interpretable way. Then, a multi stream joint distribution adaptation algorithm is proposed to optimize the consequent part of the TSK fuzzy system, which learns the final fuzzy shared representations for different streams. Hence, this method concurrently investigates both the commonalities across streams and the distinctive information within each stream. Following that, window-based and GMM-based online adaptation strategies are designed to address the asynchronous drifts over time. The former can directly demonstrate the effectiveness of FSRL in knowledge transfer across multiple streams, while the GMM-based method offers an informed way to overcome the asynchronous drift problem by integrating drift detection and adaptation. Finally, extensive experiments on several synthetic and real-world benchmarks with concept drift demonstrate the proposed method's effectiveness and efficiency.
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