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Analysis on Misclassification in Existing Contraction of Fuzzy Min–Max Models

  • Essam AlhroobEmail author
  • Mohammed Falah Mohammed
  • Osama Nayel Al Sayaydeh
  • Fadhl Hujainah
  • Ngahzaifa Ab Ghani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)

Abstract

Fuzzy min–max (FMM) neural network is one of the most useful models for pattern classification. Various models have been introduced based on FMM model to improve the classification performance. However, the misclassification of the contraction process is a crucial issue that has to be handled in FMM models to improve classification accuracy. Hence, this research aims to analyse the existence and execution procedure of addressing the misclassification of the contraction in the current FMM models. In this manner, practitioners and researchers are aided in selecting the convenient model that can address the misclassification of the contraction and improve the performance of models in producing accurate classification results. A total of 15 existing FMM models are identified and analysed in terms of the contraction problem. Results reveal that only five models can address the contraction misclassification problem. However, these models suffer from serious limitations, including the inability to detect all overlap cases, and increasing the network structure complexity. A new model is thus needed to address the specified limitations for increasing the pattern classification accuracy.

Keywords

Patten classification Misclassification Fuzzy min- max FMM models 

Notes

Acknowledgement

The researchers would like to express their sincere gratitude for the financial support of the Fundamental Research Grant Scheme (FRGS/1/2018/ICT02/UMP/02/15) and University Malaysia Pahang grant (RDU180369).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Essam Alhroob
    • 1
    Email author
  • Mohammed Falah Mohammed
    • 1
    • 2
  • Osama Nayel Al Sayaydeh
    • 1
  • Fadhl Hujainah
    • 1
  • Ngahzaifa Ab Ghani
    • 1
  1. 1.Faculty of Computer Systems and Software EngineeringUniversiti Malaysia PahangKuantanMalaysia
  2. 2.Faculty of Science, Department of Computer ScienceUniversity of ZakhoDuhokIraq

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