A Numerical Classification Technique Based on Fuzzy Soft Set Using Hamming Distance

  • Iwan Tri Riyadi Yanto
  • Rd Rohmat Saedudin
  • Saima Anwar Lashari
  • Haviluddin
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 700)


In recent decades, fuzzy soft set techniques and approaches have received a great deal of attention from practitioners and soft computing researchers. This article attempts to introduce a classifier for numerical data using similarity measure fuzzy soft set (FSS) based on Hamming distance, named HDFSSC. Dataset have been taken from UCI Machine Learning Repository and MIAS (Mammographic Image Analysis Society). The proposed modeling consists of four phases: data acquisition, feature fuzzification, training phase and testing phase. Later, head to head comparison between state of the art fuzzy soft set classifiers is provided. Experiment results showed that the proposed classifier provides better accuracy when compared to the baseline fuzzy soft set classifiers.


Fuzzy soft set (FSS) Similarity measure Hamming distance, classification 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Iwan Tri Riyadi Yanto
    • 1
  • Rd Rohmat Saedudin
    • 2
  • Saima Anwar Lashari
    • 3
  • Haviluddin
    • 4
  1. 1.Department of Information SystemsUniversity of Ahmad Dahlan, Kampus III UADYogyakartaIndonesia
  2. 2.School of Industrial EngineeringTelkom UniversityBandungIndonesia
  3. 3.Faculty of Computer Science and Information TechnologyUniversiti Tun Hussein OnnParit RajaMalaysia
  4. 4.Faculty of Computer Science and Information TechnologyMulawarman UniversitySamarindaIndonesia

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