Sphere Classification for Ambiguous Data
Yi-meng Lin, Xuan Wang, Wing W. Y. Ng, Qun Chang, Daniel Yeung, Xiaolong Wang · 2006
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.