Sphere Classification for Ambiguous Data

Author

Lin, Yi-meng ; Wang, Xuan ; Ng, Wing W Y ; Chang, Qun ; Yeung, Daniel S. ; Wang, Xiao-long

Author_Institution

Media & Life Sci. Comput. Lab., Harbin Inst. of Technol., Shenzhen

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2571

Lastpage

2574

Abstract

In some cases, an ambiguous pattern may belong to more than one class, however it is forcibly classified to one of these classes in conventional support vector machine. Handling those ambiguous patterns in this way may loss the uncertainty information of the patterns. Therefore, we prefer to keep the uncertainty information in the ambiguous patterns. In this work, instead of two-class classification, we propose to classify samples into four classes: namely positive, negative, ambiguous and outlier classes

Keywords

pattern classification; support vector machines; ambiguous data pattern; sphere classification; support vector machine; uncertainty information; Cancer detection; Cybernetics; Electronic mail; Laboratories; Machine learning; Support vector machine classification; Support vector machines; Uncertainty; Unsupervised learning; Ambiguous and Uncertainty in Sample; Hyperplane; Sphere Classification; Support Vector Machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258851

Filename

4028497

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2894031