Shadowed sets-based sample selection method for fuzzy support vector machine.

Sample selection can speed up the training of Fuzzy Support Vector Machine(SVM). However, it is difficult to select effective sample and the selection ratio is very high. This paper proposes a new sample selection method for Fuzzy SVM based on shadowed sets. We divide the fuzzy sets into three subsets, i.e. trustable data sets, trustless data sets and uncertain data sets. The samples are only selected in trustable data sets and uncertain data sets by using the subspace selection algorithm and the border vector extraction method respectively. Experimental results show that the training time and selection ratio is significantly reduced without any decrease in generalization ability by using the samples chosen by the proposed method. Furthermore, it improves the prediction performance of the classifiers when the data sets contain noises. [ABSTRACT FROM AUTHOR]