A novel Data-driven fuzzy aggregation method for Takagi-Sugeno-Kang fuzzy Neural network system using ensemble learning

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Abstract

Fuzzy systems can be successfully employed to model vague or imprecise information in order to solve multi-attribute decision-making problems with uncertainty in the real world. This paper proposes a novel data-driven fuzzy aggregation method for Takagi-Sugeno-Kang fuzzy neural networks (TSKFNN) based upon the ensemble learning algorithm AdaBoost. The objective of this research is to investigate whether ensemble learning is an effective tool for data-driven fuzzy aggregation. Our hypothesis is that ensemble learning would improve model performance and explainability. In this study, AdaBoost is applied to get a weighted combination of fuzzy rules in the TSKFNN and calculate the weighted average of these fuzzy rules to generate model predictions. Existing fuzzy aggregation operators are used as benchmarks to evaluate the proposed model. The results show that the proposed model is capable of yielding crisp values with higher accuracy and greater interpretability than the existing methods through the identification of the most significant fuzzy rules used in the decision making process.
Original languageEnglish
Title of host publicationIEEE International Conference on Fuzzy Systems (FUZZ-IEEE): Proceedings
Publisher IEEE
ISBN (Electronic)978-1-6654-4407-1
ISBN (Print)978-1-6654-4408-8
DOIs
Publication statusPublished - 05 Aug 2021
EventIEEE International Conf. on Fuzzy Systems 2021
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Duration: 11 Jul 202114 Jul 2021
https://attend.ieee.org/fuzzieee-2021/

Publication series

Name1558-4739
PublisherIEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
ISSN (Print)1544-5615
ISSN (Electronic)1558-4739

Conference

ConferenceIEEE International Conf. on Fuzzy Systems 2021
Abbreviated titleFUZZ-IEEE 2021
Period11/07/202114/07/2021
Internet address

Keywords

  • Fuzzy aggregation
  • AdaBoost
  • TSK Fuzzy-Neural network
  • multi-attribute decision-making
  • ensemble learning
  • Fuzzy neural networks
  • decision making

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