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A Fuzzy Universum Support Vector Machine Based on Information Entropy

  • B. Richhariya
  • M. Tanveer
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

Universum-based support vector machines (USVMs) are known to give better generalization performance than standard SVM methods by incorporating prior information about the data. In datasets involving noise and outliers, this universum-based scheme is not so effective because the generated universum data points do not lie in between the two classes. In this paper, we propose a fuzzy universum support vector machine (FUSVM) by introducing the weights to the universum data points based on their information entropy. Since there is no standard approach of selecting the universum, our information entropy based approach is helpful in giving less weight to the outlier universum points and thus gives prior information about the data in an appropriate manner. In addition, we also propose a fuzzy-based approach for universum twin support vector machine named as fuzzy universum twin support vector machine (FUTSVM). Experimental results on several benchmark datasets indicate that, comparing to SVM, USVM, TWSVM and UTSVM our proposed FUSVM and FUTSVM have shown better generalization performance.

Keywords

Universum Fuzzy membership Information entropy K-nearest neighbour (KNN) 

Notes

Acknowledgements

This work was supported by Science and Engineering Research Board (SERB) as Early Career Research Award grant no. ECR/2017/000053 and Department of Science and Technology as Ramanujan fellowship grant no. SB/S2/RJN-001/2016. We gratefully acknowledge the Indian Institute of Technology Indore for providing facilities and support.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Discipline of MathematicsIndian Institute of Technology IndoreSimrol, IndoreIndia

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