Abstract
In this paper, we discuss fuzzy least squares support vector machines that resolve unclassifiable regions for multiclass problems. We define a membership function in the direction perpendicular to the optimal separating hyperplane that separates a pair of classes. Using the minimum or average operation for these membership functions, we define a membership function for each class. Using the blood cell data, we show that recognition performance of fuzzy LS-SVMs with the minimum operator is superior to that of fuzzy SVMs. While, although the performance of fuzzy LS-SVMs with average operator is inferior to that of fuzzy LS-SVMs with minimum operator, it is comparable to that of fuzzy SVMs.
