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Fuzzy Rule-Based Classification with Hypersphere Information Granules

  • Chen FuEmail author
  • Wei Lu
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1000)

Abstract

Fuzzy rule-based classification has been studied by a number of classification architectures. In this study, hypersphere information granules are used to form initial fuzzy classification model in an intuitive and interpretative way. The principle of justifiable granularity offers a certain way to optimizing information granules while facing the coverage and specificity criteria. By engaging a synergy of the principle of justifiable granularity and migrating prototypes, the refined classification model is constructed for better classification performance. A series of experiments concerning synthetic datasets and comparative studies are also implemented to exhibit the feasibility and effectiveness of the proposed classification method.

Notes

Acknowledgements

This research was supported by the Natural Science Foundation of China under Grant No. 61876029.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Control Science and EngineeringDalian University of TechnologyDalian CityPeople’s Republic of China

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