Fuzzy Comprehensive Evaluation Algorithm

In many evaluation algorithms, methods that compare with optimal discriminant sets are widely utilized. However, these approaches frequently require integration with other algorithms to achieve more precise results. One such integration method involves the Analytic Hierarchy Process (AHP). This technique facilitates pairwise comparisons of multiple factors to quantify their relative influence on decision outcomes. By combining AHP with optimal discriminant set comparison methods, developers can implement a more comprehensive assessment framework using weight calculation functions and matrix operations. This hybrid approach typically involves implementing fuzzy membership functions for factor scoring, eigenvalue calculations for consistency verification, and weighted aggregation algorithms for final evaluation scores. The integration significantly improves decision quality assessment while enhancing both accuracy and reliability through systematic multi-criteria analysis.