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145509 - Distributed semi-supervised fuzzy regression with interpolation consistency regularization.pdf (466.01 kB)

Distributed semi-supervised fuzzy regression with interpolation consistency regularization

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journal contribution
posted on 2023-05-21, 01:02 authored by Shi, Y, Zhang, L, Cao, Z, Tanveer, M, Lin, C-T
Recently, distributed semisupervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate nonsensitive information with their neighbors. However, existing DSSL algorithms cannot cope with data uncertainties and may suffer from high computation and communication overhead problems. To handle these issues, we propose a distributed semisupervised fuzzy regression (DSFR) model with fuzzy if-then rules and interpolation consistency regularization (ICR). The ICR, which was proposed recently for semisupervised problem, can force decision boundaries to pass through sparse data areas, thus increasing model robustness. However, its application in distributed scenarios has not been considered yet. In this work, we proposed a distributed fuzzy C-means (DFCM) method and a distributed interpolation consistency regularization (DICR) built on the well-known alternating direction method of multipliers to respectively locate parameters in antecedent and consequent components of DSFR. Notably, the DSFR model converges very fast since it does not involve back-propagation procedure and is scalable to large-scale datasets benefiting from the utilization of DFCM and DICR. Experiments results on both artificial and real-world datasets show that the proposed DSFR model can achieve much better performance than the state-of-the-art DSSL algorithm in terms of both loss value and computational cost. Our code is available online.


Publication title

IEEE Transactions on Fuzzy Systems






3125 - 3137




School of Information and Communication Technology


Ieee-Inst Electrical Electronics Engineers Inc

Place of publication

445 Hoes Lane, Piscataway, USA, Nj, 08855

Rights statement

(c) 2021 IEEE

Repository Status

  • Open

Socio-economic Objectives

Artificial intelligence