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A Novel Approach for Data Classification Using Neutrosophic Entropy

  • Kanika Bhutani
  • Swati Aggarwal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Classification Fuzzy probability Fuzzy entropy Neutrosophic probability Neutrosophic entropy 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringNIT KurukshetraKurukshetraIndia
  2. 2.COENSITDwarkaIndia

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