A New Support Vector Data Description with Fuzzy Constraints

Author

GhasemiGol, Mohammad ; Sabzekar, Mostafa ; Monsefi, Reza ; Naghibzadeh, Mahmoud ; Yazdi, Hadi Sadoghi

Author_Institution

Dept. of Comput. Eng., Ferdowsi Univ. of Mashhad (FUM), Mashhad, Iran

fYear

2010

fDate

27-29 Jan. 2010

Firstpage

10

Lastpage

14

Abstract

This paper presents a novel approach to eliminate the effect of noisy samples from the learning step of support vector data description (SVDD) method. SVDD is a popular kernel method which tries to fit a hypersphere around the target object and can obtain more flexible and more accurate data descriptions by using proper kernel functions. Nonetheless, the SVDD could sometimes generate such a loose decision boundary while some noisy samples (outliers) exist in the training set. In order to solve this problem we define fuzzy constraints and two new concepts for each learning sample. Duo to the usage of fuzzy constraints, we called this method fuzzy constraints SVDD (FCSVDD). The overall experiments show prominence of our proposed method in comparison with the standard SVDD.

Keywords

data handling; fuzzy set theory; support vector machines; fuzzy constraints; learning sample; noisy samples; proper kernel functions; support vector data description; Computational modeling; Computer simulation; Data engineering; Fuzzy systems; Intelligent systems; Kernel; Principal component analysis; Reconstruction algorithms; Support vector machine classification; Support vector machines; Fuzzy constraints; One-class classification; Support Vector Data Description;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on

Conference_Location

Liverpool

Print_ISBN

978-1-4244-5984-1

Type

conf

DOI

10.1109/ISMS.2010.13

Filename

5416130