Fuzzy Constraints Linear Discriminant Analysis

TL;DR

This paper introduces FC-LDA, a fuzzy constraint linear discriminant analysis method that minimizes misclassification errors, handles uncertainty with fuzzy resources, and outperforms traditional LDA in noisy data classification.

Contribution

The paper presents a novel FC-LDA approach that incorporates fuzzy linear programming to improve classification accuracy and robustness against noisy data.

Findings

FC-LDA reduces misclassification errors effectively.

The method handles noisy data with varying tolerance levels.

FC-LDA outperforms traditional LDA in classification tasks.

Abstract

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.