Title :
Fuzzy Multi-sphere Support Vector Data Description
Author :
Le, Trung ; Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra
Author_Institution :
Fac. of Inf. Sci. & Eng., Univ. of Canberra, Canberra, ACT, Australia
Abstract :
Multi-sphere Support Vector Data Description (MS-SVDD) has been proposed in our previous work. MS-SVDD aims to build a set of spherically shaped boundaries that provide a better data description to the normal dataset and an iterative learning algorithm that determines the set of spherically shaped boundaries. MS-SVDD could improve classification rate for one-class classification problems comparing with SVDD. However MS-SVDD requires a small abnormal data set to build the spherically shaped boundaries for the normal data set. In this paper, we propose a new fuzzy MS-SVDD that can be used when only the normal data set is available. Experimental results on 14 well-known datasets and a comparison between fuzzy MS-SVDD and SVDD are also presented.
Keywords :
data handling; fuzzy set theory; iterative methods; learning (artificial intelligence); support vector machines; Fuzzy multisphere support vector data description; MS-SVDD; data description; iterative learning algorithm; spherically shaped boundaries; Data models; Iterative methods; Kernel; Machine learning; Optimization; Support vector machines; Vectors; Novelty detection; fuzzy model; one-class classification; support vector data description;
Conference_Titel :
Fuzzy Systems (FUZZ-IEEE), 2012 IEEE International Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1507-4
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZ-IEEE.2012.6251336
