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System and Architecture pp 305–317Cite as

A Novel Approach for Data Classification Using Neutrosophic Entropy

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 732))

Abstract

Fuzzy classification is very necessary because it has the ability to use interpretable rules. It has got control over the limitations of crisp rule-based classification. This paper mainly deals with classification using fuzzy probability and Neutrosophic probability. Classification based on Neutrosophic probability employs Neutrosophic logic, Neutrosophic probability, and Neutrosophic entropy for its working and is compared with classification based on fuzzy probability on the basis of parameters such as probability and ambiguity in the results. Classification based on fuzzy and Neutrosophic probabilities is implemented on appendicitis dataset from knowledge extraction based on evolutionary learning.

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Author information

Authors and Affiliations

  1. Department of Computer Engineering, NIT Kurukshetra, Kurukshetra, India

    Kanika Bhutani

  2. COE, NSIT, Dwarka, India

    Swati Aggarwal

Authors
  1. Kanika Bhutani
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  2. Swati Aggarwal
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Corresponding author

Correspondence to Kanika Bhutani .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Delhi, Delhi, India

    Sunil Kumar Muttoo

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Bhutani, K., Aggarwal, S. (2018). A Novel Approach for Data Classification Using Neutrosophic Entropy. In: Muttoo, S. (eds) System and Architecture. Advances in Intelligent Systems and Computing, vol 732. Springer, Singapore. https://doi.org/10.1007/978-981-10-8533-8_29

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  • DOI: https://doi.org/10.1007/978-981-10-8533-8_29

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8532-1

  • Online ISBN: 978-981-10-8533-8

  • eBook Packages: EngineeringEngineering (R0)

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