mlsanityData to 14 Oct 2026

Survey on Deep Fuzzy Systems in regression applications: a view on interpretability

arXiv 2209.04230 ↗First posted 9 Sep 2022cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Regression problems have been more and more embraced by deep learning (DL) techniques. The increasing number of papers recently published in this domain, including surveys and reviews, shows that deep regression has captured the attention of the community due to efficiency and good accuracy in systems with high-dimensional data. However, many DL methodologies have complex structures that are not readily transparent to human users. Accessing the interpretability of these models is an essential factor for addressing problems in sensitive areas such as cyber-security systems, medical, financial surveillance, and industrial processes. Fuzzy logic systems (FLS) are inherently interpretable models, well known in the literature, capable of using nonlinear representations for complex systems through linguistic terms with membership degrees mimicking human thought. Within an atmosphere of explainable artificial intelligence, it is necessary to consider a trade-off between accuracy and interpretability for developing intelligent models. This paper aims to investigate the state-of-the-art on existing methodologies that combine DL and FLS, namely deep fuzzy systems, to address regression problems, configuring a topic that is currently not sufficiently explored in the literature and thus deserves a comprehensive survey.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 227 references that are in the map. First posted on arXiv 9 Sep 2022.

Builds on

15 of 227 references.

References · 15

  1. 2014Deep Learning in Neural Networks: An Overviewcited by 770
  2. 2020A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospectscited by 397
  3. 2015Recent Advances in Convolutional Neural Networkscited by 360
  4. 2019Deep Learning for Anomaly Detection: A Surveycited by 358
  5. 2020Deep Learning for Anomaly Detection: A Reviewcited by 159
  6. 2016Understanding Deep Convolutional Networkscited by 76
  7. 2013How to Construct Deep Recurrent Neural Networkscited by 64
  8. 2019Towards Explainable Deep Neural Networks (xDNN)cited by 61
  9. 2021Robust Optimization and Validation of Echo State Networks for learning chaotic dynamicscited by 27
  10. 2019A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trendscited by 26
  11. 2018Deep Echo State Networks with Uncertainty Quantification for Spatio-Temporal Forecastingcited by 16
  12. 2017Prediction of Sea Surface Temperature using Long Short-Term Memorycited by 9
3 more
  1. 2019Heuristic design of fuzzy inference systems: A review of three decades of researchcited by 2
  2. 2017Comparison of multi-task convolutional neural network (MT-CNN) and a few other methods for toxicity predictioncited by 1
  3. 2019Deep Fuzzy Systemscited by 1

Built on by

Not cited yet.