  In this paper we introduce a fuzzy constraint linear discriminant analysis
(FC-LDA). The FC-LDA tries to minimize misclassification error based on
modified perceptron criterion that benefits handling the uncertainty near the
decision boundary by means of a fuzzy linear programming approach with fuzzy
resources. The method proposed has low computational complexity because of its
linear characteristics and the ability to deal with noisy data with different
degrees of tolerance. Obtained results verify the success of the algorithm when
dealing with different problems. Comparing FC-LDA and LDA shows superiority in
classification task.
