Rough Set Attribute Reduction Library for MATLAB

This MATLAB rough set attribute reduction library provides essential functions for handling attribute reduction tasks using rough set theory. The implementation includes specialized M-files that perform critical operations: TargetOptFcn.m serves as the fitness function for genetic algorithm-based attribute selection, evaluating solution quality during optimization. PositiveRegion.m implements the positive region calculation crucial for determining attribute dependencies. LowerApproximation.m computes lower approximations of sets according to rough set principles. IsSub.m provides efficient subset verification between two sets using logical operations. EquivalentClassSet.m generates the complete set of equivalence classes (U/R) based on specified attribute classifications. EleEquivalentSet.m identifies specific equivalence classes containing target elements through element-class mapping. DependencyDegree.m quantifies attribute dependencies by measuring how strongly decision attributes depend on condition attributes. The library supports complete rough set analysis workflows, from basic set operations to advanced attribute reduction algorithms. Each function follows MATLAB best practices with vectorized operations where possible, ensuring computational efficiency for large datasets. The implementation includes error handling and validation checks to maintain robustness during equivalence class computations and dependency measurements.