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Intuitionistic Fuzzy Universum Support Vector Machine

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Neural Information Processing (ICONIP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13623))

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Abstract

The classical support vector machine is an effective classification technique. It solves a convex optimization problem to give a global solution. But it suffers from noise and outliers. To deal with this, an intuitionistic fuzzy number (IFN) is assigned to the training samples which reduces the effect of noise. In this paper, we propose intuitionistic fuzzy universum support vector machine (IFUSVM), where IFN is assigned to the training data points in presence of universum data. Universum points lead to prior knowledge about data distribution and assignment of IFN to the data points reduces the effect of outliers and noise. Thus, leading to the enhanced generalization property of the model. Numerical experimental results and statistical analysis over 17 binary benchmark UCI datasets show the superiority of the proposed model over the baseline models in terms of rank and accuracy.

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Acknowledgment

This work is supported and funded by science and Engineering Research Board (SERB) under Mathematical Research Impact-Centric Support (MATRICS) scheme grant no. MTR/2021/000787 and National Supercomputing mission under DST and Miety, Govt. of India with Grant number DST/NSM/RD HPC Appll/2021/03.29. Ms. Anuradha Kumari (File no - 09/1022 (12437)/2021-EMR-I) expresses her gratitude to Council of Scientific and Industrial Research (CSIR), New Delhi, India for the financial support provided as fellowship. We are grateful to Indian Institute of Technology Indore, India for providing the facilities and support.

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Authors and Affiliations

  1. Department of Mathematics, Indian Institute of Technology Indore, Simrol, 453552, Indore, India

    Anuradha Kumari, M. A. Ganaie & M. Tanveer

Authors
  1. Anuradha Kumari
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  2. M. A. Ganaie
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  3. M. Tanveer
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Corresponding author

Correspondence to Anuradha Kumari .

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Editors and Affiliations

  1. Indian Institute of Technology Indore, Indore, India

    Mohammad Tanveer

  2. Indian Institute of Information Technology - Allahabad, Prayagraj, India

    Sonali Agarwal

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

  4. Indian Institute of Technology Patna, Patna, India

    Asif Ekbal

  5. University of Innsbruck, Innsbruck, Austria

    Adam Jatowt

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Kumari, A., Ganaie, M.A., Tanveer, M. (2023). Intuitionistic Fuzzy Universum Support Vector Machine. In: Tanveer, M., Agarwal, S., Ozawa, S., Ekbal, A., Jatowt, A. (eds) Neural Information Processing. ICONIP 2022. Lecture Notes in Computer Science, vol 13623. Springer, Cham. https://doi.org/10.1007/978-3-031-30105-6_20

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  • DOI: https://doi.org/10.1007/978-3-031-30105-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30104-9

  • Online ISBN: 978-3-031-30105-6

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