FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

  • Kamran KowsariEmail author
  • Nima Bari
  • Roman Vichr
  • Farhad A. Goodarzi
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 887)


This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.


Fuzzy logic Supervised learning Binary feature Learning algorithms Big data Classification task 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Kamran Kowsari
    • 1
    Email author
  • Nima Bari
    • 2
  • Roman Vichr
    • 3
  • Farhad A. Goodarzi
    • 4
  1. 1.Department of Computer ScienceUniversity of VirginiaCharlottesvilleUSA
  2. 2.Department of Computer ScienceThe George Washington UniversityWashingtonUSA
  3. 3.Data Mining and Surveillance and Metaknowledge DiscoveryFairfaxUSA
  4. 4.Department of Mechanical and Aerospace EngineeringThe George Washington UniversityWashingtonUSA

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