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Optimizing Support Vector Machines for Multi-class Classification

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Advances in Computing and Data Sciences (ICACDS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 721))

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Abstract

The accuracy obtained when classifying multi-class data depends on the classifier and the features used for training the classifier. The parameters passed to the classifier and feature selection techniques can help improve accuracy. In this paper we propose certain dataset and classifier optimization to help improve the accuracy when classifying multi-class data. These optimization also help in reducing the training time.

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Author information

Authors and Affiliations

  1. Department of Mathematics, BITS Pilani-K.K Birla Goa Campus, NH-17B, Bye Pass Road, Zuarinagar, Goa, India

    J. K. Sahoo & Akhil Balaji

Authors
  1. J. K. Sahoo
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  2. Akhil Balaji
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Corresponding author

Correspondence to J. K. Sahoo .

Editor information

Editors and Affiliations

  1. Krishna Engineering College, Ghaziabad, Uttar Pradesh, India

    Mayank Singh

  2. Jaypee University of Information Technology, Waknaghat, Himachal Pradesh, India

    P.K. Gupta

  3. Department of Computer Science and Engineering, Raghogarh, Jaypee University of Engineering and Technology, Guna, Madhya Pradesh, India

    Vipin Tyagi

  4. Indira Gandhi Delhi Technical University, New Delhi, India

    Arun Sharma

  5. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Ontario, Canada

    Tuncer Ören

  6. Department of Computer and Information Science, University of Michigan-Dearborn, Dearborn, Michigan, USA

    William Grosky

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Sahoo, J.K., Balaji, A. (2017). Optimizing Support Vector Machines for Multi-class Classification. In: Singh, M., Gupta, P., Tyagi, V., Sharma, A., Ören, T., Grosky, W. (eds) Advances in Computing and Data Sciences. ICACDS 2016. Communications in Computer and Information Science, vol 721. Springer, Singapore. https://doi.org/10.1007/978-981-10-5427-3_42

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  • DOI: https://doi.org/10.1007/978-981-10-5427-3_42

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-5426-6

  • Online ISBN: 978-981-10-5427-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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