Knowledge and Information Systems

, Volume 61, Issue 2, pp 821–846 | Cite as

A novel fuzzy rule extraction approach using Gaussian kernel-based granular computing

  • Guangyao Dai
  • Yi Hu
  • Yu Yang
  • Nanxun Zhang
  • Ajith Abraham
  • Hongbo LiuEmail author
Regular Paper


In this paper, we present a novel fuzzy rule extraction approach by employing the Gaussian kernels and fuzzy concept lattices. First we introduce the Gaussian kernel to interval type-2 fuzzy rough sets to model fuzzy similarity relations and introduce a few concepts and theorems to improve the classification performance with fewer attributes accordingly. Based on this idea, we propose a novel attribute reduction algorithm, which can achieve better classification performance of deducing reduction subset of fewer attributes, and this will be used in the subsequent decision rule extraction. Then we justify the necessary and sufficient conditions of our fuzzy rule extraction approach through three implicit rule theorems and present a novel fuzzy decision rule extraction algorithm using fuzzy concept lattices and introduce the concepts of frequent nodes and candidate 2-tuples to our pruning strategy. Also, comparative performance experiments are carried out on the UCI datasets, and the results of both reduction subset size and classification ability show the advantages of our algorithm.


Interval type-2 fuzzy rough sets Gaussian kernel Fuzzy formal concept Fuzzy similarity relation Granular computing 



The authors would like to thank Zongmei Wang and Chao Yang for their scientific collaboration in this research. This work is partly supported by the Program for New Century Excellent Talents in University (NCET-11-0861) and the National Natural Science Foundation of China (Grant Nos. 61472058, 61602086, 61702291 and 61772102).

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Information Science and TechnologyDalian Maritime UniversityDalianChina
  2. 2.Department of Mathematical SciencesGeorgia Southern UniversityStatesboroUSA
  3. 3.Machine Intelligence Research LabsScientific Network for Innovation and Research ExcellenceAuburnUSA

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