Fuzzy Constraints Linear Discriminant Analysis
This work addresses classification robustness for noisy data, but it appears incremental as it builds on existing LDA methods with fuzzy constraints.
The paper tackles classification uncertainty near decision boundaries by introducing Fuzzy Constraints Linear Discriminant Analysis (FC-LDA), which uses a fuzzy linear programming approach to minimize misclassification error, resulting in superior performance compared to standard LDA in classification tasks.
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.