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Lee, Seulki
Embedded Artificial Intelligence Lab.
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dc.citation.conferencePlace SP -
dc.citation.conferencePlace 바르셀로나, 스페인 -
dc.citation.endPage 1382 -
dc.citation.startPage 1372 -
dc.citation.title International Conference on Knowledge Discovery and Data Mining -
dc.contributor.author Kim, Jaeho -
dc.contributor.author Hahn, Seok-Ju -
dc.contributor.author Hwang, Yoontae -
dc.contributor.author Lee, Junghye -
dc.contributor.author Lee, Seulki -
dc.date.accessioned 2024-09-20T09:35:09Z -
dc.date.available 2024-09-20T09:35:09Z -
dc.date.created 2024-09-19 -
dc.date.issued 2024-08-28 -
dc.description.abstract In multivariate time series (MTS) classification, finding the important features (e.g., sensors) for model performance is crucial yet challenging due to the complex, high-dimensional nature of MTS data, intricate temporal dynamics, and the necessity for domain-specific interpretations. Current explanation methods for MTS mostly focus on time-centric explanations, apt for pinpointing important time periods but less effective in identifying key features. This limitation underscores the pressing need for a feature-centric approach, a vital yet often overlooked perspective that complements timecentric analysis. To bridge this gap, our study introduces a novel feature-centric explanation and evaluation framework for MTS, named CAFO (Channel Attention and Feature Orthgonalization). CAFO employs a convolution-based approach with channel attention mechanisms, incorporating a depth-wise separable channel attention module (DepCA) and a QR decomposition-based loss for promoting feature-wise orthogonality. We demonstrate that this orthogonalization enhances the separability of attention distributions, thereby refining and stabilizing the ranking of feature importance. This improvement in feature-wise ranking enhances our understanding of feature explainability in MTS. Furthermore, we develop metrics to evaluate global and class-specific feature importance. Our framework’s efficacy is validated through extensive empirical analyses on two major public benchmarks and real-world datasets, both synthetic and self-collected, specifically designed to highlight class-wise discriminative features. The results confirm CAFO’s robustness and informative capacity in assessing feature importance in MTS classification tasks. This study not only advances the understanding of feature-centric explanations in MTS but also sets a foundation for future explorations in feature-centric explanations. The codes are available at https://github.com/eai-lab/CAFO. -
dc.identifier.bibliographicCitation International Conference on Knowledge Discovery and Data Mining, pp.1372 - 1382 -
dc.identifier.doi 10.1145/3637528.3671724 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/83846 -
dc.identifier.url https://dl.acm.org/doi/10.1145/3637528.3671724 -
dc.language 한국어 -
dc.publisher Association for Computing Machinery -
dc.title CAFO: Feature-Centric Explanation on Time Series Classification -
dc.type Conference Paper -
dc.date.conferenceDate 2024-08-25 -

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