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

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Advances in Information and Communication Networks (FICC 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 887))

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Abstract

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.

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Notes

  1. 1.

    The National Aeronautics and Space Administration.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Virginia, Charlottesville, VA, USA

    Kamran Kowsari

  2. Department of Computer Science, The George Washington University, Washington, USA

    Nima Bari

  3. Data Mining and Surveillance and Metaknowledge Discovery, Fairfax, USA

    Roman Vichr

  4. Department of Mechanical and Aerospace Engineering, The George Washington University, Washington, USA

    Farhad A. Goodarzi

Authors
  1. Kamran Kowsari
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  2. Nima Bari
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  3. Roman Vichr
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  4. Farhad A. Goodarzi
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Corresponding author

Correspondence to Kamran Kowsari .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, New Delhi, India

    Rahul Bhatia

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Kowsari, K., Bari, N., Vichr, R., Goodarzi, F.A. (2019). FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Advances in Information and Communication Networks. FICC 2018. Advances in Intelligent Systems and Computing, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-030-03405-4_46

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  • DOI: https://doi.org/10.1007/978-3-030-03405-4_46

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  • Online ISBN: 978-3-030-03405-4

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