A Neuro-Fuzzy Classification System Using Dynamic Clustering

  • Heisnam Rohen SinghEmail author
  • Saroj Kr Biswas
  • Biswajit Purkayastha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)


Classification task provides a deep insight into the data and helps in better understanding and effective decision-making. It is mostly associated with feature selection for better performance. Various techniques are used for classification; however, they provide poor explanation and understandability. Neuro-fuzzy techniques are most suitable for better understandability. In the neuro-fuzzy system, the features are interpreted with some linguistic form. In these existing neuro-fuzzy systems, numbers of linguistic variables are produced for each input. This leads to more computational, limited explanation, and understandability to the generated classification rules. In this, a neuro-fuzzy system is suggested for rule-based classification and the novelty lies in the way significant linguistic variables are generated, and it results in better transparency and accuracy of classification rules. The performance of the proposed system is tested with eight benchmark datasets from UCI repository.


Classification Neural network Fuzzy logic Neuro-fuzzy system Clustering Linguistic variable selection 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Heisnam Rohen Singh
    • 1
    Email author
  • Saroj Kr Biswas
    • 1
  • Biswajit Purkayastha
    • 1
  1. 1.NITSilcharIndia

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