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International Conference on Computer Vision and Image Processing

CVIP 2019: Computer Vision and Image Processing pp 74–85Cite as

A Deep Neural Network Classifier Based on Belief Theory

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1148))

Abstract

Classification is a machine learning technique that is used to find the membership of an object to a given set of classes or groups. Neural network (NN) classifier always suffers some issues while examining outliers and data points from nearby classes. In this paper, we present a new hyper-credal neural network classifier based on belief theory. This method is based on credal classification technique introduced in Dempster-Shafer Theory (DST). It allows a data point to belong not only to a specific class but also to a meta-class or an ignorant class based on its mass. The sample which lies in an overlapping region is accurately classified in this method to a meta-class which corresponds to the union of the overlapping classes. Therefore this approach reduces the classification error at the price of precision. But this decrease in precision is acceptable in applications such as medical, defence related applications where a wrong decision would cost more than avoiding some correct decisions. The results and analysis of different databases are given to illustrate the potential of this approach. This idea of hyper-credal classification is extended to convolutional neural network (CNN) classifiers also.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, NIT Calicut, Kozhikode, India

    Minny George & Praveen Sankaran

Authors
  1. Minny George
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  2. Praveen Sankaran
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Corresponding author

Correspondence to Minny George .

Editor information

Editors and Affiliations

  1. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Neeta Nain

  2. Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Santosh Kumar Vipparthi

  3. Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Balasubramanian Raman

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Cite this paper

George, M., Sankaran, P. (2020). A Deep Neural Network Classifier Based on Belief Theory. In: Nain, N., Vipparthi, S., Raman, B. (eds) Computer Vision and Image Processing. CVIP 2019. Communications in Computer and Information Science, vol 1148. Springer, Singapore. https://doi.org/10.1007/978-981-15-4018-9_7

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  • DOI: https://doi.org/10.1007/978-981-15-4018-9_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-4017-2

  • Online ISBN: 978-981-15-4018-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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