Abstract
Interpretability in Deep Learning has become a critical component in applied AI research. When it comes to understanding deep learning in time-series contexts, many approaches emphasize visualization methods and post-hoc techniques. Conversely, the EcFNN approach integrates a deep learning model with a fuzzy logic system. This system generates fuzzy rules to unveil the black-box nature of the decision-making of the embedded neural network. These linguistic fuzzy rules are simpler for humans to understand. However, the EcFNN does not support multivariate time-series problems. In this paper, we develop a method called E-EcFNN that supports multivariate time-series problems. Notably, our experiments indicate that our new method provides interpretability and maintains a competitive level of accuracy compared to other baselines.
This work was supported in part by European Commission under MISO project (grant 101086541), EEA grant under the HAPADS project (grant NOR/POLNOR/HAPADS/0049/2019-00), Research Council of Norway under eX3 project (grant 270053) and Sigma2 (grant NN9342K).
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La, HL., Ngoc-Nha Tran, V., Manh La, H., Hoai Ha, P. (2024). Interpretable Fuzzy Embedded Neural Network for Multivariate Time-Series Forecasting. In: Nguyen, N.T., et al. Intelligent Information and Database Systems. ACIIDS 2024. Lecture Notes in Computer Science(), vol 14796. Springer, Singapore. https://doi.org/10.1007/978-981-97-4985-0_25
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DOI: https://doi.org/10.1007/978-981-97-4985-0_25
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