Title :
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
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;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258851
