EconPapers    
Economics at your fingertips  
 

An intuitionistic fuzzy set based S $$^3$$ 3 VM model for binary classification with mislabeled information

Ye Tian, Zhibin Deng (zhibindeng@ucas.ac.cn), Jian Luo (luojian546@hotmail.com) and Yueqing Li
Additional contact information
Ye Tian: Southwestern University of Finance and Economics
Zhibin Deng: University of Chinese Academy of Sciences
Jian Luo: Dongbei University of Finance and Economics
Yueqing Li: Lamar University

Fuzzy Optimization and Decision Making, 2018, vol. 17, issue 4, No 6, 475-494

Abstract: Abstract Traditionally, robust and fuzzy support vector machine models are used to handle the binary classification problem with noise and outliers. These models in general suffer from the negative effects of having mislabeled training points and disregard position information. In this paper, we propose a novel method to better address these issues. First, we adopt the intuitionistic fuzzy set approach to detect suspectable mislabeled training points. Then we omit their labels but use their full position information to build a semi-supervised support vector machine ( $$\mathrm {S^3VM}$$ S 3 VM ) model. After that, we reformulate the corresponding model into a non-convex problem and design a branch-and-bound algorithm to solve it. A new lower bound estimator is used to improve the accuracy and efficiency for binary classification. Numerical tests are conducted to compare the performances of the proposed method with other benchmark support vector machine models. The results strongly support the superior performance of the proposed method.

Keywords: Binary classification; Mislabeled information; Intuitionistic fuzzy set; Semi-supervised support vector machine; Branch-and-bound algorithm; Lower bound estimator (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://link.springer.com/10.1007/s10700-017-9282-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fuzodm:v:17:y:2018:i:4:d:10.1007_s10700-017-9282-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10700

DOI: 10.1007/s10700-017-9282-z

Access Statistics for this article

Fuzzy Optimization and Decision Making is currently edited by Shu-Cherng Fang and Boading Liu

More articles in Fuzzy Optimization and Decision Making from Springer
Bibliographic data for series maintained by Sonal Shukla (sonal.shukla@springer.com) and Springer Nature Abstracting and Indexing (indexing@springernature.com).

 
Page updated 2024-12-29
Handle: RePEc:spr:fuzodm:v:17:y:2018:i:4:d:10.1007_s10700-017-9282-z