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Entropy-Based Fuzzy Least Squares Twin Support Vector Machine for Pattern Classification

  • Sugen ChenEmail author
  • Junfeng Cao
  • Fenglin Chen
  • Bingbing Liu
Article

Abstract

Least squares twin support vector machine (LSTSVM) is a new machine learning method, as opposed to solving two quadratic programming problems in twin support vector machine (TWSVM), which generates two nonparallel hyperplanes by solving a pair of linear system of equations. However, LSTSVM obtains the resultant classifier by giving same importance to all training samples which may be important for classification performance. In this paper, by considering the fuzzy membership value for each sample, we propose an entropy-based fuzzy least squares twin support vector machine where fuzzy membership values are assigned based on the entropy values of all training samples. The proposed method not only retains the superior characteristics of LSTSVM which is simple and fast algorithm, but also implements the structural risk minimization principle to overcome the possible over- fitting problem. Experiments are performed on several synthetic as well as benchmark datasets and the experimental results illustrate the effectiveness of our method.

Keywords

Pattern classification Information entropy Least squares twin support vector machine Fuzzy membership 

Notes

Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Grant No. 61702012), the University Outstanding Young Talent Support Project of Anhui Province of China (Grant No. gxyq2017026), the University Natural Science Research Project of Anhui Province of China (Grant Nos. KJ2016A431, KJ2017A361 and KJ2017A368) and the Program for Innovative Research Team in Anqing Normal University.

Compliance with Ethical Standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mathematics and Computational ScienceAnqing Normal UniversityAnqingPeople’s Republic of China
  2. 2.Key Laboratory of Modeling, Simulation and Control of Complex Ecosystem in Dabie Mountains of Anhui Higher Education InstitutesAnqing Normal UniversityAnqingChina
  3. 3.School of ScienceJiangnan UniversityWuxiPeople’s Republic of China

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