Conference Paper2019Cited by 1

Evidential SVM for binary classification

Md Eusha Kadir, Pritom Saha Akash, Amin Ahsan Ali, Mohammad Shoyaib, Zerina Begum

2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT)

IEEE, pp. 1–6

DOI: 10.1109/icasert.2019.8934772URL

Abstract

Support Vector Machine (SVM) is one of the most popular supervised learning methods for its better performances over diversified applications. SVM constructs a maximum-margin hyperplane and predicts the class of a new incoming data point based on that hyperplane. However, the hyperplane may not give a reliable decision in some cases specially when the training data is imprecise and noisy. To improve this situation, we propose an evidence based binary SVM classifier (EbSVM) where we first identify the sources of information (SoI) and propose a method to generate mass value from these SoIs. Finally, we combine these mass values using DS theory of evidence. An experiment over six benchmark datasets illustrates that EbSVM significantly performs better than the existing state-of-the-art methods.

Keywords

  • Dempster-Shafer theory
  • Binary classification
  • noisy training data
  • information sources
  • classifier robustness
  • mass function

CCDS Authors

Amin Ahsan Ali, PhD
Amin Ahsan Ali, PhD

Amin Ahsan Ali, PhD

  • Director, AI/ML Wing

Professor

Department of Computer Science and Engineering

Independent University, Bangladesh

Published at

ACL, CHI, UbiComp, ECCV, IJCAI, ECAI, WACV, IPSN, PAKDD, LREC, IJCNN, ICPR, ICIP, BEA, BLP, ICMLA, Scientific Reports, Scientific Data, Pattern Recognition, Information Sciences, Ad Hoc Networks, Drug and Alcohol Dependence, PLOS ONE

Research Interests

Artificial Intelligence, Machine Learning

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Cited By

  1. 1.(2019). ICASERT 2019 Table of Contents. 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), i–xx[10.1109/icasert.2019.8934687]
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